inproceedings.bib
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@comment{{Command line: bib2bib -ob inproceedings.bib -oc inproceedings -c $type="INPROCEEDINGS" kruse-all.bib}}
@inproceedings{Steinbrecher2009,
author = {Steinbrecher, Matthias
and Kruse, Rudolf},
editor = {Mertsching, B{\"a}rbel
and Hund, Marcus
and Aziz, Zaheer},
title = {Assessing the Strength of Structural Changes in Cooccurrence Graphs: Advances in Artificial Intelligence, 32nd Annual German Conference on AI, Paderborn, Germany},
series = {Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence},
year = {2009},
publisher = {Springer Verlag},
volume = {5803},
pages = {476--483},
isbn = {978-3-642-04616-2}
}
@inproceedings{A.1997c,
author = {N{\"u}rnberger, Andreas
and Nauck, Detlef
and Merz, L.
and Kruse, Rudolf},
title = {A neuro-fuzzy development tool for fuzzy controllers under MATLAB/SIMULINK},
year = {1997},
pages = {1029--1033}
}
@inproceedings{A.1997d,
author = {N{\"u}rnberger, Andreas
and Kruse, Rudolf},
title = {Learning methods for fuzzy systems},
year = {1997}
}
@inproceedings{A.1997f,
author = {N{\"u}rnberger, Andreas
and Kruse, Rudolf
and Nauck, Detlef},
title = {Neuro-Fuzzy-Regelung mit NEFCON unter MATLAB/SIMULINK},
year = {1997},
publisher = {Universit{\"a}t Stuttgart}
}
@inproceedings{A.1998b,
author = {N{\"u}rnberger, Andreas
and Radetzky, A.
and Kruse, Rudolf},
title = {A Problem Specific Recurrent Neural Network for the Description and Simulation of Dynamic Spring Models},
year = {1998},
pages = {468--473}
}
@inproceedings{A.1998c,
author = {N{\"u}rnberger, Andreas
and Radetzky, A.
and Kruse, Rudolf},
title = {Modelling and Simulating a Time-Dependent Physical System Using Fuzzy Techniques and a Recurrent Neural Network},
year = {1998},
publisher = {infix},
pages = {306--313}
}
@inproceedings{A.1998d,
author = {N{\"u}rnberger, Andreas
and Kruse, Rudolf},
title = {Neuro-Fuzzy Techniques under MATLAB/SIMULINK Applied to a Real Plant},
year = {1998},
pages = {572--576}
}
@inproceedings{A.1998e,
author = {Radetzky, A.
and N{\"u}rnberger, Andreas
and Pretschner, D. P.
and Kruse, Rudolf},
title = {The Simulation of Elastic Tissues in Virtual Laparoscopy using Neural Networks},
year = {1998},
pages = {167--174}
}
@inproceedings{A.1999a,
author = {N{\"u}rnberger, Andreas
and Klose, Aljoscha
and Kruse, Rudolf},
title = {Discussing Cluster Shapes of Fuzzy Classifiers},
year = {1999},
publisher = {New York},
pages = {546--550}
}
@inproceedings{A.1999b,
author = {N{\"u}rnberger, Andreas
and Radetzky, A.
and Kruse, Rudolf},
title = {Determination of Elastodynamic Model Parameters using a Recurrent Neuro-Fuzzy System},
year = {1999},
publisher = {Verlag Mainz}
}
@inproceedings{A.1999c,
author = {N{\"u}rnberger, Andreas
and Klose, Aljoscha
and Nauck, Detlef
and Kruse, Rudolf},
title = {Improving the Clarity of Neuro-Fuzzy Classifiers},
year = {1999}
}
@inproceedings{A.2000,
author = {N{\"u}rnberger, Andreas
and Klose, Aljoscha
and Kruse, Rudolf},
title = {Analyzing Borders Between Partially Contradicting Fuzzy Classification Rules},
year = {2000},
pages = {59--63}
}
@inproceedings{A.2000a,
author = {Klose, Aljoscha
and Kruse, Rudolf
and Gross, H.
and Thoennessen, U.},
title = {Automatische Adaption Struktureller Bildanalysealgorithmen unter Verwendung von Data Mining Techniken},
year = {2000},
publisher = {VDI-Verlag},
pages = {91--96}
}
@inproceedings{A.2000b,
author = {Klose, Aljoscha
and Kruse, Rudolf
and Schulz, K.
and Thoennessen, U.},
title = {Controlling Asymmetric Errors in Neuro-Fuzzy Classification},
year = {2000},
publisher = {ACM Press}
}
@inproceedings{A.2000c,
author = {Klose, Aljoscha
and Kruse, Rudolf
and Gross, H.
and Thoennessen, U.},
title = {Tuning on the Fly of Structural Image Analysis Algorithms Using Data Mining},
year = {2000},
publisher = {SPIE Press}
}
@inproceedings{A.2000d,
author = {N{\"u}rnberger, Andreas
and Kruse, Rudolf
and Klose, Aljoscha},
title = {Effects of Antecedent Pruning in Fuzzy Classification Systems},
year = {2000},
pages = {154--157}
}
@inproceedings{A.2002e,
author = {Klose, Aljoscha
and Girimonte, Daniela
and Kruse, Rudolf},
title = {Extending Neuro Fuzzy systems to Semi-supervised Learning},
year = {2002}
}
@inproceedings{A.2003a,
author = {Eichhorn, A.
and Girimonte, Daniela
and Klose, Aljoscha
and Kruse, Rudolf},
title = {Neuro-Fuzzy Classification of Surface Form Deviations},
year = {2003},
pages = {902--907}
}
@inproceedings{Berthold_et_al_2003,
editor = {Berthold, Michael R.
and Lenz, Hans-Joachim
and Bradley, Elizabeth
and Kruse, Rudolf
and Borgelt, Christian},
title = {Advances in Intelligent Data Analysis V --- Proc.{\backslash} 5th Int.{\backslash} Symp.{\backslash} on Intelligent Data Analysis (IDA2003, Berlin, Germany)},
year = {2003},
publisher = {Springer-Verlag},
url = {http://www.springerlink.com/openurl.asp?genre=issue&issn=0302-9743&volume=2810}
}
@inproceedings{boettcher2008icdm,
author = {B{\"o}ttcher, Mirko
and Spott, Martin
and Kruse, Rudolf},
title = {Predicting Future Decision Trees from Evolving Data},
year = {2008},
publisher = {IEEE Computer Society},
address = {Pisa, Italy},
pages = {33--42},
abstract = {Recognizing and analyzing change is an important human virtue because it enables us to anticipate future scenarios and thus allows us to act pro-actively. One approach to understand change within a domain is to analyze how modelsand patterns evolve. Knowing how a model changes over time is suggesting to ask: Can we use this knowledge to learn a model in anticipation, such that it better reflects the near-future characteristics of an evolving domain? In this paper we provide an answer to this question by presenting an algorithm which predicts future decision trees based ona model of change. In particular, this algorithm encompasses a novel approach to change mining which is based on analyzing the changes of the decisions made during model learning. The proposed approach can also be applied to other types of classifiers and thus provides a basis for future research. We present our first experimental results which show that anticipated decision trees have the potential to outperform trees learned on the most recent data.},
isbn = {978-0-7695-3502-9},
doi = {10.1109/icdm.2008.90}
}
@inproceedings{boettcher2009conceptdrifting,
author = {B{\"o}ttcher, Mirko
and Spott, Martin
and Kruse, Rudolf},
editor = {Bramer, Max
and Coenen, Frans
and Petridis, Miltos},
title = {An Algorithm for Anticipating Future Decision Trees from Concept-Drifting Data},
series = {Proceedings of AI-2008},
year = {2009},
publisher = {Springer},
address = {London},
volume = {25},
pages = {293--306},
isbn = {978-1-84882-170-5}
}
@inproceedings{boettcher2009pkdd,
author = {B{\"o}ttcher, Mirko
and Spott, Martin
and Kruse, Rudolf},
title = {A Condensed Representation of Itemsets for Analyzing their Evolution over Time},
series = {Lecture Notes in Artificial Intelligence (LNAI)},
year = {2009},
publisher = {Springer},
abstract = {Driven by the need to understand change within domains there is emerging research on methods which aim at analyzing how patterns and in particular itemsets evolve over time. In practice, however, these methods suffer from the problem that many of the observed changes in itemsets are temporally redundant in the sense that they are the side-effect of changes in other itemsets, hence making the identification of the fundamental changes difficult. As a solution we propose temporally closed itemsets, a novel approach for a condensed representation of itemsets which is based on removing temporal redundancies. We investigate how our approach relates to the well-known concept of closed itemsets if the latter would be directly generalized to account for the temporal dimension. Our experiments support the theoretical results by showing that the set of temporally closed itemsets is significantly smaller than the set of closed itemsets.},
note = {(to appear)}
}
@inproceedings{Borgelt_and_Kruse_1998c,
author = {Borgelt, Christian
and Kruse, Rudolf},
title = {Efficient Maximum Projection of Database-Induced Multivariate Possibility Distributions},
year = {1998},
publisher = {IEEE Press},
volume = {1},
pages = {663--668}
}
@inproceedings{Borgelt_and_Kruse_1998d,
author = {Borgelt, Christian
and Kruse, Rudolf},
title = {Possibilistic Networks with Local Structure},
year = {1998},
publisher = {Verlag Mainz},
volume = {1},
pages = {634--638}
}
@inproceedings{Borgelt_and_Kruse_1999a,
author = {Borgelt, Christian
and Kruse, Rudolf},
title = {A Critique of Inductive Causation},
year = {1999},
publisher = {Springer-Verlag},
pages = {68--79}
}
@inproceedings{Borgelt_and_Kruse_2000b,
author = {Borgelt, Christian
and Kruse, Rudolf},
title = {Learning from Imprecise Data: Possibilistic Graphical Models},
year = {2000},
publisher = {Consiglio Nazionale delle Ricerche},
pages = {190--203}
}
@inproceedings{Borgelt_and_Kruse_2001b,
author = {Borgelt, Christian
and Kruse, Rudolf},
editor = {Benferhat, S.
and Besnard, P.},
title = {An Empirical Investigation of the \{K2\} Metric},
year = {2001},
publisher = {Springer-Verlag},
pages = {240--251}
}
@inproceedings{Borgelt_and_Kruse_2001c,
author = {Borgelt, Christian
and Kruse, Rudolf},
title = {Learning Graphical Models with Hypertree Structure Using a Simulated Annealing Approach},
year = {2001},
publisher = {IEEE Press}
}
@inproceedings{Borgelt_and_Kruse_2003c,
author = {Borgelt, Christian
and Kruse, Rudolf},
title = {Speeding Up Fuzzy Clustering with Neural Network Techniques},
year = {2003},
publisher = {IEEE Press}
}
@inproceedings{Borgelt_and_Kruse_2004b,
author = {Borgelt, Christian
and Kruse, Rudolf},
title = {Shape and Size Regularization in Expectation Maximization and Fuzzy Clustering},
year = {2004},
publisher = {Springer-Verlag},
volume = {3202/2004},
pages = {52--62},
abstract = {The more sophisticated fuzzy clustering algorithms, like the Gustafson?Kessel algorithm [11] and the fuzzy maximum likelihood estimation (FMLE) algorithm [10] offer the possibility of inducing clusters of ellipsoidal shape and different sizes. The same holds for the EM algorithm for a mixture of Gaussians. However, these additional degrees of freedom often reduce the robustness of the algorithm, thus sometimes rendering their application problematic. In this paper we suggest shape and size regularization methods that handle this problem effectively.},
doi = {10.1007/b100704},
url = {http://borgelt.net/papers/pkdd_04.pdf}
}
@inproceedings{Borgelt_and_Kruse_2005a,
author = {Borgelt, Christian
and Kruse, Rudolf},
title = {Fuzzy and Probabilistic Clustering with Shape and Size Constraints},
year = {2005},
publisher = {Tsinghua University Press and Springer-Verlag},
pages = {945--950}
}
@inproceedings{Borgelt_and_Kruse_2005b,
author = {Borgelt, Christian
and Kruse, Rudolf},
title = {Probabilistic Graphical Models for the Diagnosis of Analog Electrical Circuits},
year = {2005},
publisher = {Springer-Verlag},
pages = {100--110},
url = {http://www.springerlink.com/app/home/contribution.asp?wasp=333e72c03b0941f8a133a60dbf3c8408&referrer=parent&backto=issue,10,85;journal,14,2099;linkingpublicationresults,1:105633,1}
}
@inproceedings{Borgelt_and_Kruse_2006b,
author = {Borgelt, Christian
and Kruse, Rudolf},
title = {Finding the Number of Fuzzy Clusters by Resampling},
year = {2006},
publisher = {IEEE Press, Piscataway, NJ, USA},
doi = {10.1109/FUZZY.2006.1681693},
url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?tp=&arnumber=1681693&isnumber=35437}
}
@inproceedings{Borgelt_et_al_1996,
author = {Borgelt, Christian
and Gebhardt, J{\"o}rg
and Kruse, Rudolf},
title = {Concepts for Probabilistic and Possibilistic Induction of Decision Trees on Real World Data},
year = {1996},
publisher = {Verlag Mainz},
volume = {3},
pages = {1556--1560}
}
@inproceedings{Borgelt_et_al_2000a,
author = {Borgelt, Christian
and Timm, Heiko
and Kruse, Rudolf},
title = {Using Fuzzy Clustering to Improve Naive \{B\}ayes Classifiers and Probabilistic Networks},
year = {2000},
publisher = {IEEE Press}
}
@inproceedings{Borgelt_et_al_2000c,
author = {Borgelt, Christian
and Gebhardt, J{\"o}rg
and Kruse, Rudolf},
editor = {Riccia, Giacomo Della
and Kruse, Rudolf
and Lenz, Hans-Joachim},
title = {Possibilistic Graphical Models},
series = {CISM Courses and Lectures},
year = {2000},
publisher = {Springer-Verlag},
volume = {408},
pages = {51--68}
}
@inproceedings{Borgelt_et_al_2005a,
author = {Borgelt, Christian
and N{\"u}rnberger, Andreas
and Kruse, Rudolf},
title = {Fuzzy Learning Vector Quantization with Size and Shape Parameters},
year = {2005},
publisher = {IEEE Press},
pages = {195--200},
abstract = {We study an extension of fuzzy learning vector quantization that draws on ideas from the more sophisticated approaches to fuzzy clustering, enabling us to find fuzzy clusters of ellipsoidal shape and differing size with a competitive learning scheme. This approach may be seen as a kind of online fuzzy clustering, which can have advantages w.r.t. the execution time of the clustering algorithm. We demonstrate the usefulness of our approach by applying it to document collections, which are, in general, difficult to cluster due to the high number of dimensions and the special distribution characteristics of the data},
doi = {10.1109/fuzzy.2005.1452392},
url = {http://borgelt.net/papers/fieee_05.pdf}
}
@inproceedings{Borgelt_Kruse_FuzzyClustering_2007NAFIPS,
author = {Borgelt, Christian
and Kruse, Rudolf},
title = {An Extended Objective Function for Prototype-less Fuzzy Clustering},
year = {2007},
pages = {146--151}
}
@inproceedings{C.1997,
author = {Borgelt, Christian
and Kruse, Rudolf},
title = {Learning probabilistic and possibilistic networks: Theory and applications},
year = {1997},
volume = {1},
pages = {19--24}
}
@inproceedings{C.1998c,
author = {Borgelt, Christian
and Gebhardt, J{\"o}rg
and Kruse, Rudolf},
title = {Possibilistic Graphical Models},
year = {1998}
}
@inproceedings{C.2002a,
author = {Borgelt, Christian
and Kruse, Rudolf},
title = {Learning Graphical Models by Extending Optimal Spanning Trees IPMU'02},
year = {2002},
url = {http://www.borgelt.net/papers/ipmu_02.pdf}
}
@inproceedings{D.1992a,
author = {Nauck, Detlef
and Klawonn, Frank
and Kruse, Rudolf},
title = {Fuzzy Sets, Fuzzy Controller and Neural Networks},
year = {1992}
}
@inproceedings{D.1996b,
author = {Kruse, Rudolf
and Nauck, Detlef},
editor = {Paa{\ss}, Gerhard
and M{\"o}ller, Knut
and Dorffner, G.
and Vogel, S.
and Rojas, R.},
title = {Neuronale Fuzzy-Systeme},
series = {Beitr{\"a}ge zur Herbstschule (HeKoNN96), GMD-Studien Nr. 300},
year = {1996},
publisher = {GMD-Forschungszentrum Informatik GmbH},
pages = {157--170}
}
@inproceedings{D.1996c,
author = {Nauck, Detlef
and Nauck, Ulrike
and Kruse, Rudolf},
title = {Generating Classification Rules with the Neuro-Fuzzy System (NEFCLASS)},
year = {1996},
publisher = {IEEE, Berkeley},
pages = {466--470}
}
@inproceedings{D.1998b,
author = {Nauck, Detlef
and Kruse, Rudolf},
title = {Rule Weights in Fuzzy Systems},
year = {1998}
}
@inproceedings{D.1998c,
author = {Nauck, Detlef
and N{\"u}rnberger, Andreas
and Kruse, Rudolf},
title = {Neuro-Fuzzy Classification},
year = {1998},
publisher = {Springer-Verlag},
pages = {287--294}
}
@inproceedings{D.1999c,
author = {Nauck, Detlef
and Kruse, Rudolf},
editor = {Brewka, G.
and Gottwald, R. Der
and Schierwagen, A.},
title = {Fuzzy Classification Rules Using Categorical and Metric Variables},
year = {1999},
publisher = {Leipziger Universit{\"a}tsverlag},
pages = {133--144}
}
@inproceedings{D.1999d,
author = {Nauck, Detlef
and Kruse, Rudolf},
title = {Fusing Expert Knowledge and Information from Data with NEFCLASS},
year = {1999},
publisher = {Sunnyvale, CA},
pages = {386--393}
}
@inproceedings{D.1999e,
author = {Nauck, Detlef
and Kruse, Rudolf},
title = {Learning in Neuro-Fuzzy Systems with Symbolic Attributes and Missing Values},
year = {1999},
publisher = {Perth},
pages = {142--147}
}
@inproceedings{D.1999f,
author = {Nauck, Detlef
and Nauck, Ulrike
and Kruse, Rudolf},
title = {NEFCLASS for JAVA -- New Learning Algorithms},
year = {1999},
publisher = {IEEE},
pages = {472--476}
}
@inproceedings{Detlef1998,
author = {Nauck, Detlef
and Kruse, Rudolf},
title = {A Neuro-Fuzzy Approach to Obtain Interpretable Fuzzy Systems for Function Approximation},
year = {1998},
pages = {1106--1111}
}
@inproceedings{Doering_et_al_2004,
author = {D{\"o}ring, Christian
and Borgelt, Christian
and Kruse, Rudolf},
title = {Fuzzy Clustering of Quantitative and Qualitative Data},
year = {2004},
publisher = {IEEE Press},
pages = {84--89}
}
@inproceedings{Doering_et_al_2005,
author = {D{\"o}ring, Christian
and Borgelt, Christian
and Kruse, Rudolf},
title = {Effects of Irrelevant Attributes in Fuzzy Clustering},
year = {2005},
publisher = {IEEE Press},
pages = {862--866},
doi = {10.1109/FUZZY.2005.1452507}
}
@inproceedings{eichhorn2002surface,
author = {Eichhorn, A.
and Girimonte, Daniela
and Klose, Aljoscha
and Kruse, Rudolf},
title = {Surface Quality Analysis with Soft Computing},
series = {10th Zittau Fuzzy Colloquium},
year = {2002},
publisher = {IPM},
volume = {75},
pages = {292--299}
}
@inproceedings{F.1992a,
author = {Klawonn, Frank
and Gebhardt, J{\"o}rg
and Kruse, Rudolf},
title = {Logical Approaches to Uncertainty and Vagueness in the View of the Context Model},
year = {1992}
}
@inproceedings{F.1997,
author = {Klawonn, Frank
and Kruse, Rudolf},
editor = {Leondes, C. T.},
title = {Techniques and applications of control systems based on knowledge based interpolation},
series = {Fuzzy Theory: Systems, Techniques and Application},
year = {1997},
publisher = {Academic Press},
pages = {431--460}
}
@inproceedings{F.2004a,
author = {Rehm, Frank
and Klawonn, Frank
and Kruse, Rudolf},
title = {Ausrei{\backslash}\{ss\}ererkennung mit Fuzzy-Clustering-Methoden},
year = {2004},
publisher = {Universit{\"a}tsverlag Karlsruhe}
}
@inproceedings{F.2004b,
author = {Rehm, Frank
and Klawonn, Frank
and Kruse, Rudolf},
title = {New Approaches to Noise Clustering for Detecting Outliers},
year = {2004}
}
@inproceedings{Frank2005,
author = {Rehm, Frank
and Klawonn, Frank
and Kruse, Rudolf},
title = {Mdspolar: A new approach for dimension reduction to visualize high dimensional data},
series = {Lecture Notes in Computer Science},
year = {2005},
publisher = {Springer},
address = {Berlin, Heidelberg},
volume = {3646},
pages = {316--327},
abstract = {Many applications in science and business such as signal analysis or costumer segmentation deal with large amounts of data which are usually high dimensional in the feature space. As a part of preprocessing and exploratory data analysis, visualization of the data helps to decide which kind of method probably leads to good results. Since the visual assessment of a feature space that has more than three dimensions is not possible, it becomes necessary to find an appropriate visualization scheme for such datasets. In this paper we present a new approach for dimension reduction to visualize high dimensional data. Our algorithm transforms high dimensional feature vectors into two-dimensional feature vectors under the constraints that the length of each vector is preserved and that the angles between vectors approximate the corresponding angles in the high dimensional space as good as possible, enabling us to come up with an efficient computing scheme.},
isbn = {978-3-540-28795-7},
issn = {0302-9743},
doi = {10.1007/11552253_29}
}
@inproceedings{Frank2005a,
author = {Rehm, Frank
and Klawonn, Frank
and Kruse, Rudolf},
title = {Visualizing single fuzzy c-means clusters},
year = {2005},
publisher = {Gesellschaft f{\"u}r Informatik},
url = {http://elib.dlr.de/19120/}
}
@inproceedings{G.1998,
author = {Kruse, Rudolf
and Saake, G.},
title = {Proceedings des Workshops Data Mining und Data Warehousing am Rande der Informatik'98},
year = {1998},
publisher = {Universit{\"a}t Magdeburg}
}
@inproceedings{Gebhardt1990,
author = {Gebhardt, J{\"o}rg
and Kruse, Rudolf},
title = {Some New Aspects of Testing Hypotheses in Fuzzy Statistics},
year = {1990}
}
@inproceedings{Gebhardt1991,
author = {Gebhardt, J{\"o}rg
and Kruse, Rudolf},
title = {An Integrating Model of Partial Ignorance},
year = {1991}
}
@inproceedings{Gebhardt1991a,
author = {Gebhardt, J{\"o}rg
and Kruse, Rudolf},
title = {An Integrating Model of Uncertainty and Vagueness},
year = {1991}
}
@inproceedings{Gebhardt1992,
author = {Gebhardt, J{\"o}rg
and Kruse, Rudolf},
title = {A Possibilistic Interpretation of Fuzzy Sets by the Context Model},
year = {1992}
}
@inproceedings{Gebhardt1992a,
author = {Gebhardt, J{\"o}rg
and Kruse, Rudolf},
title = {Possibility Theory and the Context Model},
year = {1992}
}
@inproceedings{Gebhardt1993a,
author = {Gebhardt, J{\"o}rg
and Kruse, Rudolf},
editor = {Clarke, M.
and Kruse, Rudolf
and Moral, S.},
title = {A New Approach to Semantical Aspects of Possibilistic Reasoning},
series = {Lecture Notes in Computer Science},
year = {1993},
publisher = {Springer Verlag},
volume = {747}
}
@inproceedings{Gebhardt1994,
author = {Gebhardt, J{\"o}rg
and Kruse, Rudolf},
title = {A Numerical Framework for Possibilistic Abduction},
year = {1994}
}
@inproceedings{Gebhardt1994a,
author = {Gebhardt, J{\"o}rg
and Kruse, Rudolf},
title = {On an Information Compression View of Possibility Theory},
year = {1994},
pages = {1285--1288}
}
@inproceedings{Gebhardt1995,
author = {Gebhardt, J{\"o}rg
and Kruse, Rudolf},
title = {Learning Possibilistic Networks from Data},
year = {1995},
pages = {233--244}
}
@inproceedings{Gebhardt1995a,
author = {Gebhardt, J{\"o}rg
and Kruse, Rudolf},
title = {Learning Possibilistic Networks from Data},
year = {1995},
pages = {1575--1580}
}
@inproceedings{Gebhardt1995b,
author = {Gebhardt, J{\"o}rg
and Kruse, Rudolf},
editor = {Lavrac, N.
and Wrobel, S.},
title = {Reasoning and Learning in Probabilistic and Possibilistic Networks: \{ECML95\}, Lecture Notes in Artificial Intelligence},
year = {1995},
publisher = {Springer},
volume = {912},
pages = {3--16}
}
@inproceedings{Gebhardt1995c,
author = {Gebhardt, J{\"o}rg
and Kruse, Rudolf},
title = {Learning Possibilistic Graphical Models},
year = {1995},
pages = {74--76}
}
@inproceedings{Gebhardt1995d,
author = {Gebhardt, J{\"o}rg
and Kruse, Rudolf},
editor = {Cooman, G. De
and Ruan, D.
and Kerre, E.},
title = {Discrete Graphical Models in Possibility Theory},
year = {1995}
}
@inproceedings{Gebhardt1996,
author = {Gebhardt, J{\"o}rg
and Kruse, Rudolf},
title = {Tightest hypertree decompositions of multivariate possibility distributions},
year = {1996},
pages = {923--927}
}
@inproceedings{Gebhardt1996a,
author = {Gebhardt, J{\"o}rg
and Kruse, Rudolf},
title = {Measures of Nonspecificity for Decomposing Possibility Distributions},
year = {1996},
pages = {177--179}
}
@inproceedings{Gebhardt1996b,
author = {Gebhardt, J{\"o}rg
and Kruse, Rudolf},
title = {On a Tool for Possibilistic Reasoning in Relational Structures},
year = {1996},
pages = {1471--1475}
}
@inproceedings{Gebhardt1996c,
author = {Gebhardt, J{\"o}rg
and Kruse, Rudolf},
title = {Parallel Combination of Information Sources},
year = {1996}
}
@inproceedings{Guru_etal-2007,
author = {Guru, Siddeswara Mayura
and Steinbrecher, Matthias
and Halgamuge, Saman K.
and Kruse, Rudolf},
editor = {C{\'e}rin, Christophe
and Li, Kuan-Ching},
title = {\{Multiple Cluster Merging and Multihop Transmission in Wireless Sensor Networks\}},
series = {Lecture Notes in Computer Science},
year = {2007},
publisher = {Springer Verlag},
volume = {4459},
abstract = {Wireless sensor networks consist of sensor nodes that are deployed in a large area and collect information from a sensor field. Since the nodes have very limited energy resources, the energy consuming operations such as data collection, transmission and reception must be kept to a minimum. Low Energy Adaptive Clustering Hierarchy (LEACH) is a cluster based communication protocol where cluster-heads (CH) are used to collect data from the cluster nodes and transmit it to the remote base station. In this paper we propose two extensions to LEACH. Firstly, nodes are evenly distributed during the cluster formation process, this is accomplished by merging multiple overlapping clusters. Secondly, instead of each CH directly transmitting data to remote base station, it will do so via a CH closer to the base station. This reduces transmission energy of cluster heads. The combination of above extensions increases the data gathering at base station to 60\% for the same amount of sensor nodes energy used in LEACH.},
isbn = {978-3-540-72359-2},
issn = {0302-9743},
doi = {10.1007/978-3-540-72360-8_8}
}
@inproceedings{H.1997,
author = {Timm, Heiko
and Kruse, Rudolf
and Klawonn, Frank
and Nauck, Detlef},
title = {Flexible Fuzzy Clustering for Data Analysis as a Plug-In Library for Data Engine},
year = {1997},
pages = {67--71}
}
@inproceedings{H.1997a,
author = {Timm, Heiko
and Klawonn, Frank
and Kruse, Rudolf},
title = {Flexible Fuzzy Clustering for Data Analysis as a Plug-In Library for Data Engine},
year = {1997},
pages = {91--96}
}
@inproceedings{H.2001,
author = {D{\"o}ring, Christian
and Kruse, Rudolf
and Timm, Heiko
and Borgelt, Christian},
title = {Fuzzy Cluster Analysis with Cluster Repulsion},
year = {2001},
note = {On CD-ROM}
}
@inproceedings{H.2002,
author = {Timm, Heiko
and Klawonn, Frank
and Kruse, Rudolf},
title = {An Extension of Partially Supervised Fuzzy Cluster},
year = {2002}
}
@inproceedings{H.2002a,
author = {Timm, Heiko
and D{\"o}ring, Christian
and Kruse, Rudolf},
editor = {Kuhl, J.
and Lackner, A.},
title = {Fuzzy Clusteranalyse von Daten mit fehlenden Werten},
year = {2002},
pages = {99--112},
note = {J. Biethahn, AFN}
}
@inproceedings{H.2003,
author = {Timm, Heiko
and D{\"o}ring, Christian
and Kruse, Rudolf},
editor = {Bilgic, T.},
title = {Differentiated Treatment of Missing Values in Fuzzy Clustering},
year = {2003},
pages = {354--361},
note = {LNAI 2715}
}
@inproceedings{J.1988,
author = {Gebhardt, J{\"o}rg
and Kruse, Rudolf},
editor = {Pressmar, D.},
title = {Statistische Untersuchungen anhand von vagen Daten},
year = {1988},
publisher = {Springer Verlag}
}
@inproceedings{J.1991,
author = {Gebhardt, J{\"o}rg
and Nauck, Detlef
and Kruse, Rudolf},
title = {Interpretation und Analyse von Fuzzy Daten},
year = {1991}
}
@inproceedings{J.1992,
author = {Gebhardt, J{\"o}rg
and Kruse, Rudolf
and Nauck, Detlef},
title = {Information Compression in the Context Model},
year = {1992}
}
@inproceedings{J.1992a,
author = {Gebhardt, J{\"o}rg
and Kruse, Rudolf},
title = {Zur Interpretation von Fuzzy Controllern},
year = {1992}
}
@inproceedings{J.1993,
author = {Gebhardt, J{\"o}rg
and Kruse, Rudolf
and Otte, Clemens
and Schr{\"o}der, M.},
title = {A Fuzzy Idle Speed Controller},
year = {1993}
}
@inproceedings{J.1994,
author = {Kinzel, Jens
and Kruse, Rudolf
and Klawonn, Frank},
editor = {Reusch, B.},
title = {Anpassung Genetischer Algorithmen zum Erlernen und Optimieren von Fuzzy-Reglern},
year = {1994},
publisher = {Springer Verlag}
}
@inproceedings{J.1994a,
author = {Kinzel, Jens
and Klawonn, Frank
and Kruse, Rudolf},
title = {Modifications of Genetic Algorithms for Designing and Optimizing Fuzzy Controllers},
year = {1994},
pages = {28--33}
}
@inproceedings{J.1994b,
author = {Beckmann, J.
and Gebhardt, J{\"o}rg
and Klawonn, Frank
and Kruse, Rudolf},
title = {Possibilistic Inference and Data Fusion},
year = {1994},
pages = {46--47}
}
@inproceedings{J.1995a,
author = {Gebhardt, J{\"o}rg
and Kruse, Rudolf},
editor = {Bouchon-Meunier, B.
and Yager, Ronald R.
and Zadeh, Lotfi A.},
title = {A Numerical Framework for Possibilistic Abduction},
series = {Advances in Intelligent Computing},
year = {1995},
publisher = {Springer},
url = {http://www.springerlink.com/index/468t6ux5r1313533.pdf}
}
@inproceedings{J.1995b,
author = {Kruse, Rudolf
and Heinsohn, J.},
title = {Unsicherheit und Vagheit},
year = {1995}
}
@inproceedings{J.1998a,
author = {Gebhardt, J{\"o}rg
and Kruse, Rudolf},
editor = {Arabnia, Hamid R.
and Zhu, Dongping (Daniel)},
title = {Information Source Modelling for Consistent Data Fusion},
year = {1998},
publisher = {CSREA Press},
pages = {27--34}
}
@inproceedings{J.2000,
author = {Marx-G{\'o}mez, J.
and Rautenstrauch, C.
and N{\"u}rnberger, Andreas
and Kruse, Rudolf},
title = {Hybrid Approach to Forecast Returns of Scrapped Products to Recycling and Remanufacturing},
year = {2000}
}
@inproceedings{kempe2007gfkl,
author = {Kempe, Steffen
and Hipp, Jochen
and Kruse, Rudolf},
editor = {Preisach, Christine
and Burkhardt, Hans
and Schmidt-Thieme, Lars
and Decker, Reinhold},
title = {FSMTree: An Efficient Algorithm for Mining Frequent Temporal Patterns},
series = {Studies in Classification, Data Analysis, and Knowledge Organization},
year = {2007},
publisher = {Springer},
address = {Berlin, Heidelberg},
pages = {253--260},
abstract = {FSMTree: An Efficient Algorithm for Mining Frequent Temporal Patterns},
isbn = {978-3-540-78239-1},
issn = {1431-8814},
doi = {10.1007/978-3-540-78246-9_30},
url = {http://www.springerlink.com/index/q474057805m034u6.pdf}
}
@inproceedings{kempe2008ipmu,
author = {Kempe, Steffen
and Kruse, Rudolf},
editor = {Verdegay, Jos{\'e} Luis
and Ojeda-Aciego, Manuel
and Magdalena, Luis},
title = {Mining Temporal Patterns in an Automotive Environment},
year = {2008},
address = {M{\'a}laga},
pages = {521--528},
keywords = {frequent temporal patterns},
keywords = {industrial application},
abstract = {Mining frequent temporal patterns from interval-based data proved to be a valuable tool for generating knowledge in the automotive business. Many problems in our domain contain a temporal component and thus can be formulated by using interval sequences. In this paper we present three substantially different applications which can all be addressed by the same mining task: mining of frequent temporal patterns. We show that contemporary approaches for temporal pattern mining are not addressing this task sufficiently and present our algorithmic solution FSMTree. Further, we discuss the assessment of temporal rules which can be derived from the set of frequent patterns.}
}
@inproceedings{Klawonn1993a,
author = {Klawonn, Frank
and Kruse, Rudolf},
title = {Fuzzy Control as Interpolation on the Basis of Equality Relations},
year = {1993}
}
@inproceedings{Klawonn1994a,
author = {Klawonn, Frank
and Kruse, Rudolf},
title = {Fuzzy Partitions and Transformations},
year = {1994}
}
@inproceedings{Klawonn1995a,
author = {Klawonn, Frank
and Kruse, Rudolf},
title = {From Fuzzy Sets to Indistinguishability and Back},
year = {1995}
}
@inproceedings{Klawonn1995c,
author = {Klawonn, Frank
and Kruse, Rudolf},
title = {Automatic Generation of Fuzzy Controllers by Fuzzy Clustering},
year = {1995}
}
@inproceedings{klawonnetal95a,
author = {Klawonn, Frank
and Nauck, Detlef
and Kruse, Rudolf},
title = {Generating Rules from Data by Fuzzy and Neuro--Fuzzy Methods},
year = {1995},
pages = {223--230}
}
@inproceedings{KruGebRueDet2006,
author = {Kruse, Rudolf
and Gebhardt, J{\"o}rg
and R{\"u}gheimer, Frank
and Detmer, Heinz},
title = {Planning with Graphical Models},
year = {2006}
}
@inproceedings{kruse/nauck95a,
author = {Kruse, Rudolf
and Nauck, Detlef},
title = {Learning Methods for Fuzzy Systems},
year = {1995},
pages = {7--22}
}
@inproceedings{kruse/nauck95b,
author = {Kruse, Rudolf
and Nauck, Detlef},
editor = {Dorffner, Georg
and M{\"o}ller, Knut
and Paa{\ss}, Gerhard
and Vogel, Stephan},
title = {Neuronale Fuzzy--Systeme},
series = {GMD--Studien},
year = {1995},
publisher = {GMD--Forschungszentrum Informationstechnik GmbH},
number = {272},
pages = {1--10}
}
@inproceedings{Kruse1986a,
author = {Kruse, Rudolf},
title = {On a Language and an Interpreter for Calculation and Statistics on Linguistic Data},
year = {1986}
}
@inproceedings{Kruse1986b,
author = {Kruse, Rudolf
and Meyer, K. D.},
title = {Statistics with Fuzzy Data},
year = {1986}
}
@inproceedings{Kruse1986c,
author = {Kruse, Rudolf
and Meyer, K. D.},
title = {Confidence Intervals for the Parameter of the Normal Distribution in the Presence of Vague Data},
year = {1986}
}
@inproceedings{Kruse1986d,
author = {Kruse, Rudolf
and Meyer, K. D.},
title = {A consistent Variance Estimator in the Presence of Vague Data},
year = {1986}
}
@inproceedings{Kruse1987d,
author = {Kruse, Rudolf
and Meyer, K. D.},
title = {Fuzzy Markov Chains and their Application to Processor Power Considerations},
year = {1987}
}
@inproceedings{Kruse1987e,
author = {Kruse, Rudolf
and Meyer, K. D.},
title = {Parametric Statistics in the Presence of Vague Data},
year = {1987}
}
@inproceedings{Kruse1987f,
author = {Kruse, Rudolf
and Meyer, K. D.},
title = {On Linguistic Modelling and Linguistic Approximation in the Presence of Vague Data},
year = {1987}
}
@inproceedings{Kruse1988a,
author = {Kruse, Rudolf
and Meyer, K. D.},
title = {On Calculating the Covariance in the Presence of Vague Data},
year = {1988}
}
@inproceedings{Kruse1988b,
author = {Kruse, Rudolf
and Schwecke, E.},
title = {Fuzzy Reasoning in a Multidimensional Space of Hypotheses},
year = {1988},
pages = {147--151}
}
@inproceedings{Kruse1988c,
author = {Kruse, Rudolf},
title = {Evidential Reasoning in Product Spaces},
year = {1988},
address = {Reisensburg, G{\"u}nzburg}
}
@inproceedings{Kruse1989a,
author = {Kruse, Rudolf
and Gebhardt, J{\"o}rg},
title = {On a Dialog System for Modelling and Statistical Analysis of Linguistic Data},
year = {1989}
}
@inproceedings{Kruse1989b,
author = {Kruse, Rudolf
and Schwecke, E.},
title = {On the Treatment of Cyclic Dependencies in Causal Networks},
year = {1989}
}
@inproceedings{Kruse1989c,
author = {Kruse, Rudolf},
title = {Vages Wissen in Expertensystemen},
year = {1989},
address = {Bremen}
}
@inproceedings{Kruse1990b,
author = {Kruse, Rudolf
and Schwecke, E.},
title = {On the Representation of Uncertain Knowledge in the Context of Belief Functions},
year = {1990}
}
@inproceedings{Kruse1990c,
author = {Kruse, Rudolf
and Schwecke, E.},
title = {On the Combination of Information Sources},
year = {1990}
}
@inproceedings{Kruse1990d,
author = {Kruse, Rudolf
and Schwecke, E.},
title = {On the Interpretation of Conditioning Concepts for Belief Functions},
year = {1990},
address = {Reisensburg, G{\"u}nzburg}
}
@inproceedings{Kruse1991a,
author = {Kruse, Rudolf
and Gebhardt, J{\"o}rg},
title = {New Methods in Statistics with Vague Data},
year = {1991}
}
@inproceedings{Kruse1991c,
author = {Kruse, Rudolf},
title = {On the Context Model},
year = {1991},
address = {Blanes}
}
@inproceedings{Kruse1993b,
author = {Kruse, Rudolf},
title = {Fuzzy Probability Theory and Fuzzy Statistics},
year = {1993}
}
@inproceedings{Kruse1993c,
author = {Kruse, Rudolf},
title = {On the Extension of Probability Theory and Statistics to the Handling of Fuzzy Data},
year = {1993}
}
@inproceedings{Kruse1993d,
author = {Kruse, Rudolf
and Schr{\"o}der, M.},
title = {An Application of Equality Relations to Idle Speed Control},
year = {1993}
}
@inproceedings{Kruse1998,
author = {Kruse, Rudolf},
title = {Intelligente Systeme: Wie geht man mit unvollkommenen Informationen um?},
series = {Antrittsvorlesung},
year = {1998},
publisher = {Otto-von-Guericke-Universit{\"a}t},
address = {Magdeburg}
}
@inproceedings{Kruse2000a,
author = {Kruse, Rudolf
and Klose, Aljoscha},
title = {Information Mining: Applications in Image Processing},
year = {2000},
publisher = {Springer},
pages = {266--285}
}
@inproceedings{Kruse2004,
author = {Kruse, Rudolf},
title = {Soft Computing for Information Mining},
year = {2004}
}
@inproceedings{kruse2010data,
author = {Kruse, Rudolf
and Steinbrecher, Matthias
and Moewes, Christian},
editor = {Beer, Michael
and Muhanna, Rafi L.
and Mullen, Robert L.},
title = {Data Mining Applications in the Automotive Industry},
year = {2010},
publisher = {Research Publishing Services},
address = {Singapore},
pages = {23--40},
abstract = {Designing and assembling automobiles is a complex task which has to be accomplished in ever shorter cycles. However, customers have increasing desires w. r. t. reliability, durability and comfort. In order to cope with these conflicting constraints it is indispensable to employ tools that greatly simplify the analysis of data that is collected during all car lifecycle stages. We will present methods for pattern discovery tasks for the development stage, the manufacturing and planning stage as well as for maintenance and aftercare. The first approach will reinterpret a Bayesian network to induce association rules which are then visualized to find interesting patterns. The second part will use Markov networks to model the interdependencies related to the planning task when assembling a vehicle. The last part deals with finding recurring patterns in time series used for adjusting simulation parameters.},
isbn = {981-085118-9},
doi = {10.3850/978-981-08-5118-7_plenary2},
url = {http://www.rpsonline.com.sg/proceedings/9789810851187/html/plenary2.xml}
}
@inproceedings{kruse2010temporal,
author = {Kruse, Rudolf
and Steinbrecher, Matthias
and Moewes, Christian},
title = {Temporal pattern mining},
year = {2010},
publisher = {IEEE Press},
address = {Gliwice, Poland},
pages = {3--8},
abstract = {Data analysis has become an integral part in many economic fields. In this paper, we present several real-world applications occurring in the fields of automobile development and manufacturing, finance, and online communities. The given examples share one aspect in common: time. It is not only the fact to find patterns inside data volumes but also to identify them based on their temporal behaviour. We will give examples of dealing with different models of incorporating the temporal aspects. Furthermore some new results in the area of Visual Data Analysis are presented. These methods offer intuitive methods of guiding the user through the process of data and model inspection and assist in drawing conclusions with the help of meaningful graphical representations.},
isbn = {978-1-4244-5307-8},
url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5595268}
}
@inproceedings{Kruse_and_Borgelt_1998a,
author = {Borgelt, Christian
and Kruse, Rudolf},
title = {Neuere Entwicklungen im \{D\}ata \{M\}ining mit \{B\}ayesschen Netzen},
year = {1998},
publisher = {MIT GmbH}
}
@inproceedings{Kruse_and_Borgelt_1998b,
author = {Borgelt, Christian
and Kruse, Rudolf},
title = {Possibilistic Networks: Data Mining Applications},
year = {1998},
publisher = {Verlag Mainz},
volume = {1},
pages = {603--607}
}
@inproceedings{Kruse_and_Borgelt_1998c,
author = {Kruse, Rudolf
and Borgelt, Christian},
title = {Data Mining with Graphical Models},
year = {1998},
volume = {1},
pages = {17--30}
}
@inproceedings{Kruse_etal-2007a,
author = {Kruse, Rudolf
and Borgelt, Christian
and Nauck, Detlef
and and, N. J. van Eck
and Steinbrecher, Matthias},
title = {\{The role of soft computing in intelligent data analysis\}},
year = {2007},
pages = {9--17},
note = {Invited paper}
}
@inproceedings{Kruse_et_al_1999a,
author = {Kruse, Rudolf
and Borgelt, Christian
and Nauck, Detlef},
title = {Fuzzy Data Analysis: Challenges and Perspectives},
year = {1999},
publisher = {IEEE Press},
pages = {1211--1216}
}
@inproceedings{Kruse_et_al_1999b,
author = {Kruse, Rudolf
and Borgelt, Christian
and Nauck, Detlef},
title = {\{D\}ata \{M\}ining mit Neuro-\{F\}uzzy-\{S\}ystemen},
year = {1999}
}
@inproceedings{Kruse_et_al_1999c,
author = {Kruse, Rudolf
and Borgelt, Christian
and Nauck, Detlef},
title = {Data Mining with Fuzzy Methods: Status and Perspectives},
year = {1999},
publisher = {Verlag Mainz}
}
@inproceedings{L.M.1995,
author = {Campos, L. M. De
and Gebhardt, J{\"o}rg
and Kruse, Rudolf},
editor = {Froidevaux, C.
and Kohlas, J.},
title = {Axiomatic Treatment of Possibilistic Independence},
year = {1995},
publisher = {Springer},
pages = {77--88}
}
@inproceedings{Lesot2005,
author = {Lesot, Marie-Jeanne
and Kruse, Rudolf},
title = {Kernel-based outlier preserving clustering},
year = {2005},
publisher = {Universit{\"a}t G{\"o}ttingen}
}
@inproceedings{lesot2006summarisation,
author = {Lesot, M. J.
and Kruse, Rudolf},
title = {Data Summarisation by Typicality-based Clustering for Vectorial and Non Vectorial Data},
booktitle = {Fuzzy Systems, 2006 IEEE International Conference on},
year = {2006},
pages = {547--554},
abstract = {In this paper, a typicality-based clustering algorithm is proposed: it exploits typicality degrees defined in a prototype construction framework to identify a decomposition of the dataset into homogeneous and distinct clusters and to provide characteristic representatives of the obtained clusters, so as to summarise the initial dataset. The proposed algorithm can be applied both to vectorial and non vectorial data, such as trees for instance. Tests performed on artificial and real data illustrate the interest of the proposed approach.},
doi = {10.1109/fuzzy.2006.1681765}
}
@inproceedings{Lesot_etal_2006,
author = {Lesot, Marie-Jeanne
and Rehm, Frank
and Klawonn, Frank
and Kruse, Rudolf},
title = {Prediction of Aircraft Flight Duration},
year = {2006},
address = {Delft, Netherlands},
keywords = {Air Traffic Management},
keywords = {Data Mining},
keywords = {Machine Learning},
keywords = {POLARMAP},
keywords = {Support Vector Regression. Multidimensional Scaling},
url = {http://elib.dlr.de/43418}
}
@inproceedings{LNAI55900748,
author = {Beyer, J{\"o}rg
and Heesche, Kai
and Hauptmann, Werner
and Otte, Clemens
and Kruse, Rudolf},
editor = {Sossai, Claudio
and Chemello, Gaetano},
title = {Ensemble Learning for Multi-source Information Fusion},
series = {Lecture Notes in Computer Science},
year = {2009},
publisher = {Springer},
address = {Heidelberg},
volume = {5590},
pages = {748--756},
abstract = {In this paper, a new ensemble learning method is proposed. The main objective of this approach is to jointly use knowledge-based and data-driven submodels in the modeling process. The integration of knowledge-based submodels is of particular interest, since they are able to provide information not contained in the data. On the other hand, data-driven models can complement the knowledge-based models with respect to input space coverage. For the task of appropriately integrating the different models, a method for partitioning the input space for the given models is introduced. The benefits of this approach are demonstrated for a real-world application.},
isbn = {978-3-642-02905-9},
issn = {0302-9743},
doi = {10.1007/978-3-642-02906-6_64},
url = {http://www.springerlink.com/content/a1418720lx006841/}
}
@inproceedings{M.1994,
author = {Hartmann, M.
and Klawonn, Frank
and Kruse, Rudolf
and Petras, K.},
title = {Constructing Rule Bases and Fuzzy Sets for Interpolation: Experiences from Quality Evaluation},
year = {1994},
pages = {1671--1673}
}
@inproceedings{M.1995,
author = {Schr{\"o}der, M.
and Klawonn, Frank
and Kruse, Rudolf},
title = {Genetic Algorithms and Fuzzy Situations for Sequential Optimization of Control Surfaces},
year = {1995},
pages = {777--781}
}
@inproceedings{moewes08adjustung,
author = {Moewes, Christian
and Kruse, Rudolf},
editor = {Mikut, Ralf
and Reischl, Markus},
title = {Adjusting Monitored Experiments to Real-World Cases by Matching Labeled Time Series Motifs},
series = {Schriftenreihe des IAI, Universit{\"a}t Karlsruhe (TH)},
year = {2008},
publisher = {Universit{\"a}tsverlag Karlsruhe},
pages = {214--223},
keywords = {Frequent Pattern Mining},
keywords = {Labeling},
keywords = {Motif Discovery},
keywords = {Multivariate Time Series Analysis},
abstract = {In this paper we devote ourselves to the difficulty of fitting human designed experiments to real-world cases. We decompose this problem into two smaller subproblems: 1.) The search of recurrent patterns in temporal sequences, so called motifs that are deemed to be discovered in both the experiments and the real observations and 2.) the matching of motifs to linguistic terms which are possibly available as domain knowledge. Therefore we describe an effective time series representation that enormously speeds up the search for these motifs. We present some approaches to adjust the designed experiments with the help of the discovered motifs. Finally, we conclude our work and give prospects to possible extensions.},
isbn = {978-3866442825}
}
@inproceedings{moewes08tackling,
author = {Moewes, Christian
and Otte, Clemens
and Kruse, Rudolf},
editor = {Dubois, Didier
and Lubiano, M. Asunci{\'o}n
and Prade, Henri
and Gil, Mar{\'i}a {\'A}ngeles
and Grzegorzewski, Przemyslaw
and Hryniewicz, Olgierd},
title = {Tackling Multiple-Instance Problems in Safety-Related Domains by Quasilinear \{SVM\}},
series = {Advances in Soft Computing},
year = {2008},
publisher = {Springer Berlin/Heidelberg},
volume = {48},
pages = {409--416},
keywords = {Multiple-Instance Learning},
keywords = {Safety-Related Systems},
keywords = {Support Vector Machine},
abstract = {In this paper we introduce a preprocessing method for safety-related applications. Since we concentrate on scenarios with highly unbalanced misclassification costs, we briefly discuss a variation of multiple-instance learning (MIL) and recall soft margin hyperplane classifiers; in particular the principle of a support vector machine (SVM). According to this classifier, we present a training set selection method for learning quasilinear SVMs which guarantee both high accuracy and interpretability to a higher degree. We conclude with annotating on a real-world application and potential extensions for future research in this domain.},
isbn = {978-3-540-85026-7},
issn = {1615-3871},
doi = {10.1007/978-3-540-85027-4_49},
url = {http://springerlink.com/content/870h7h003w457740/}
}
@inproceedings{moewes08unification,
author = {Moewes, Christian
and Kruse, Rudolf},
editor = {Verdegay, Jos{\'e} Luis
and Magdalena, Luis
and Ojeda-Aciego, Manuel},
title = {Unification of Fuzzy \{SVMs\} and Rule Extraction Methods through imprecise Domain Knowledge},
year = {2008},
address = {Torremolinos (M{\'a}laga)},
pages = {1527--1534},
keywords = {Binary Classification},
keywords = {Fuzzy Rule-Based Classifier},
keywords = {Fuzzy Support Vector Machine},
keywords = {Support Vector Machine},
abstract = {In this paper, we want to motivate the combination of kernel-based methods with fuzzy rule extraction methods to describe uncertain domains by fuzzy models. We thus introduce and motivate the concept of a fuzzy support vector machine (FSVM) to incorporate impreciseness into kernel machines. Furthermore, we present the idea of a positive definite fuzzy classifier (PDFC), the rules of which are obtained by kernel-based models. We conclude with two vague conceptions to associate FSVM with PDFC to finally obtain understandable and meaningful fuzzy rules.}
}
@inproceedings{Nauck1992,
author = {Nauck, Detlef
and Kruse, Rudolf},
title = {Interpreting Changes in the Membership Functions of a Self Adaptive Neural Fuzzy Controller},
year = {1992}
}
@inproceedings{Nauck1992a,
author = {Nauck, Detlef
and Kruse, Rudolf},
title = {Neural Fuzzy Controller Learning by Fuzzy Error Propagation},
year = {1992}
}
@inproceedings{Nauck1995a,
author = {Nauck, Detlef
and Kruse, Rudolf},
title = {Neuro-Fuzzy Classification with NEFCLASS},
year = {1995},
publisher = {Springer-Verlag}
}
@inproceedings{Nauck1995b,
author = {Nauck, Detlef
and Kruse, Rudolf},
title = {\{NEFCLASS\} - A Neuro-Fuzzy Approach for Classification of Data},
year = {1995},
pages = {461--465},
doi = {10.1145/315891.316068}
}
@inproceedings{nauck92a,
author = {Nauck, Detlef
and Kruse, Rudolf},
title = {A Neural Fuzzy Controller Learning by Fuzzy Error Propagation},
year = {1992},
pages = {388--397}
}
@inproceedings{nauck92b,
author = {Nauck, Detlef
and Kruse, Rudolf},
title = {Interpreting Changes in the Fuzzy Sets of a Self-Adaptive Neural Fuzzy Controller},
year = {1992},
pages = {146--152}
}
@inproceedings{nauck93a,
author = {Nauck, Detlef
and Kruse, Rudolf},
title = {A Fuzzy Neural Network Learning Fuzzy Control Rules and Membership Functions by Fuzzy Error Backpropagation},
year = {1993},
pages = {1022--1027}
}
@inproceedings{nauck93b,
author = {Nauck, Detlef
and Klawonn, Frank
and Kruse, Rudolf},
editor = {Klement, Erich Peter
and Slany, Wolfgang},
title = {Combining Neural Networks and Fuzzy Controllers},
year = {1993},
publisher = {Springer--Verlag},
pages = {35--46}
}
@inproceedings{nauck94c,
author = {Nauck, Detlef
and Kruse, Rudolf},
title = {\{NEFCON--I\}: An \{X--Window\} based Simulator for Neural Fuzzy Controllers},
year = {1994},
pages = {1638--1643}
}
@inproceedings{nauck94g,
author = {Nauck, Detlef
and Kruse, Rudolf},
title = {Choosing Appropriate Neuro-Fuzzy Models},
year = {1994},
pages = {552--557}
}
@inproceedings{nauck95d,
author = {Nauck, Detlef
and Kruse, Rudolf},
editor = {Kleinschmidt, P.
and Bachem, A.
and Derigs, U.
and Fischer, D.
and Leopold-Wildburger, U.
and M{\"o}hring, R.},
title = {Neuro--Fuzzy Classification with NEFCLASS},
year = {1996},
publisher = {Springer--Verlag},
pages = {294--299}
}
@inproceedings{naucketal95a,
author = {Nauck, Detlef
and Kruse, Rudolf
and Stellmach, Roland},
title = {New Learning Algorithms for the Neuro--Fuzzy Environment \{NEFCON--I\}},
year = {1995},
pages = {357--364}
}
@inproceedings{P.1989,
author = {Friedrich, P.
and Struckmann, W.
and Kruse, Rudolf},
title = {Das Salzgittermodell - Ein Beispiel f{\"u}r die Zusammenarbeit zwischen Hochschule und Industrie bei der Entwicklung komplexer Software-Systeme},
year = {1989}
}
@inproceedings{R.1987a,
author = {Kruse, Rudolf
and Freckmann, J.
and Eike, M.},
title = {Ein Programmsystem f{\"u}r statistische Untersuchungen mit unscharfen Daten},
year = {1987}
}
@inproceedings{R.1988b,
author = {Kruse, Rudolf
and Gebhardt, J{\"o}rg
and Knop, J.},
title = {On a Dialog System for Modelling and Statistical Analysis of Linguistic Data},
year = {1988}
}
@inproceedings{R.1991b,
author = {Kruse, Rudolf
and Schwecke, E.
and Klawonn, Frank},
title = {On a Tool for Reasoning with Mass Distributions},
year = {1991}
}
@inproceedings{R.1991c,
author = {Kruse, Rudolf
and Klawonn, Frank
and Nauck, Detlef},
editor = {D'Ambrosio, B.
and Smets, Philippe
and Bonissone, P. P.},
title = {Reasoning with Mass Distributions},
year = {1991},
pages = {182--187}
}
@inproceedings{R.1991d,
author = {Kruse, Rudolf
and Gebhardt, J{\"o}rg
and Klawonn, Frank},
editor = {Kruse, Rudolf
and Siegel, P.},
title = {Reasoning with Mass Distributions and the Context Model},
series = {Lecture Notes in Computer Science},
year = {1991},
publisher = {Springer Verlag}
}
@inproceedings{R.1993a,
author = {Kruse, Rudolf
and Gebhardt, J{\"o}rg
and Klawonn, Frank},
title = {On the Interpretation of Fuzzy Controllers},
year = {1993}
}
@inproceedings{R.1994c,
author = {Kruse, Rudolf
and Gebhardt, J{\"o}rg
and Klawonn, Frank},
editor = {Deaton, E.
and Oppenheim, D.
and Urban, J.
and Berghel, H.},
title = {A Fuzzy Controller for Idle Speed Regulation},
year = {1994},
pages = {155--160}
}
@inproceedings{R.1995a,
author = {Kruse, Rudolf
and Gebhardt, J{\"o}rg},
editor = {Mammitzsch, V.
and Schneeweiss, H.},
title = {Focusing and Learning in Possibilistic Dependency Networks: Statistical Sciences)},
year = {1995},
publisher = {W.de Gruyter},
pages = {79--90}
}
@inproceedings{R.1997i,
author = {Kruse, Rudolf
and Borgelt, Christian},
title = {Evaluation measures for learning probabilistic and possibilistic networks},
year = {1997},
volume = {2},
pages = {669--676}
}
@inproceedings{R.1997j,
author = {Kruse, Rudolf
and Borgelt, Christian},
title = {Some experimental results on learning probabilistic and possibilistic networks with different evaluation measures},
year = {1997},
pages = {71--85}
}
@inproceedings{R.1997o,
author = {Kruse, Rudolf
and Siekmann, S.
and Neuneier, R.},
title = {Neuro-fuzzy methods in finance applied to the German stock index DAX},
year = {1997}
}
@inproceedings{R.1997p,
author = {Kruse, Rudolf
and Sutter, T.},
title = {Fuzzy queries in conventional databases for succession planning},
year = {1997}
}
@inproceedings{R.1997r,
author = {Kruse, Rudolf
and Nauck, Detlef},
editor = {Grauel, Adolf
and Belli, Fevzi
and Becker, Wilhelm},
title = {Neuro-fuzzy systems for function approximation},
year = {1997},
pages = {316--323}
}
@inproceedings{R.1997s,
author = {Kruse, Rudolf
and Nauck, Detlef},
title = {What are neuro-fuzzy classifiers?},
year = {1997},
volume = {3},
pages = {228--233}
}
@inproceedings{R.1997t,
author = {Kruse, Rudolf
and Nauck, Detlef},
title = {New learning strategies for NEFCLASS},
year = {1997},
volume = {4},
pages = {50--55}
}
@inproceedings{R.1997u,
author = {Kruse, Rudolf
and Nauck, Detlef},
title = {Function approximation by NEFPROX},
year = {1997},
pages = {160--169}
}
@inproceedings{R.1998j,
author = {N{\"u}rnberger, Andreas
and Kruse, Rudolf},
title = {Learning Methods for Fuzzy Systems},
year = {1998},
publisher = {IOS-Press},
pages = {367--372}
}
@inproceedings{R.1998p,
author = {Timm, Heiko
and Kruse, Rudolf},
title = {Fuzzy-Clusteranalyse mit DataEngine},
year = {1998}
}
@inproceedings{R.1998q,
author = {Kruse, Rudolf
and Timm, Heiko},
title = {Fuzzy cluster analysis with missing values},
year = {1998},
pages = {242--246}
}
@inproceedings{R.1998w,
author = {Kruse, Rudolf
and Nauck, Detlef},
title = {How the learning of rule weights affects the interpretability of fuzzy systems},
year = {1998},
pages = {1235--1240}
}
@inproceedings{R.2002,
author = {Kruse, Rudolf
and Klose, Aljoscha},
title = {Information Mining with Fuzzy Methods: Trends and Current Challenges},
year = {2002},
pages = {117--120}
}
@inproceedings{R.2002a,
author = {Kruse, Rudolf
and Borgelt, Christian},
editor = {Lange, S.},
title = {Data Mining with Graphical Models},
series = {Discovery Science},
year = {2002},
publisher = {Springer},
pages = {2--11},
note = {LNCS 2534}
}
@inproceedings{R.2003,
author = {Kruse, Rudolf
and Keller, A.},
title = {Fuzzy Rule Generation for Transfer Passenger Analysis},
year = {2003}
}
@inproceedings{R.Kruse2001,
author = {Kruse, Rudolf},
title = {Information Mining},
year = {2001},
publisher = {De Montfort University},
pages = {6--9}
}
@inproceedings{rehm2006ipmu,
author = {Rehm, Frank
and Klawonn, Frank
and Kruse, Rudolf},
title = {Rule Classification Visualization of High-Dimensional Data},
year = {2006},
abstract = {This paper presents an approach to visualize high-dimensional fuzzy classification rules and the corresponding classified data set in the plane. This enables the observer to check visually to which degree a fea- ture vector is classified by a certain rule. Also misclassified feature vec- tors can be well spotted and conflicting or error-prone rules can be iden- tified.}
}
@inproceedings{rehm2006visualization,
author = {Rehm, Frank
and Klawonn, Frank
and Kruse, Rudolf},
editor = {Rutkowski, Leszek
and Tadeusiewicz, Ryszard
and Zadeh, Lotfi A.
and Zurada, Jacek M.},
title = {Visualization of Single Clusters},
series = {Lecture Notes in Computer Science},
year = {2006},
publisher = {Springer},
volume = {4029},
pages = {663--671},
isbn = {3-540-35748-3},
doi = {10.1007/11785231_69}
}
@inproceedings{Rehm_etal_2006,
author = {Rehm, Frank
and Klawonn, Frank
and Kruse, Rudolf},
title = {Visualization of Fuzzy Rule Classifiers for Flight Duration Forecast},
year = {2006},
keywords = {Air Traffic Management},
keywords = {Fuzzy Rules},
keywords = {Luftverkehrssysteme},
keywords = {Visualization},
abstract = {The impact of the weather on the flight duration of aircraft has been analysed in various studies. The complex aspects of the weather, which are accordingly reflected in the weather data, demand sophisticated techniques to visualize the analytical results. In this paper we present an approach to visualize fuzzy rules describing high-dimensional data. By means of this method, the rules, as well as the classified data, can be presented on an arbitrary low-dimensional space. We will demonstrate the efficiency of this technique on some benchmark examples and on real weather data set that is used to predict aircraft flight duration on a European hub airport.},
note = {event\_dates=2006-09-27 - 2006-09-28;},
url = {http://elib.dlr.de/44860}
}
@inproceedings{rehm_kruse_russ_modern_data_nafips_2007,
author = {Rehm, Frank
and Kruse, Rudolf
and Ru{\ss}, Georg
and Klawonn, Frank},
title = {Modern Data Visualization for Air Traffic Management},
year = {2007},
pages = {19--24},
abstract = {Air traff ic at airports is affected by various factors. The capacity of an airport and the demand at a certain point in time are serious parameters that account for a big extent to aircraft delay and related variables. It has been proven that weather is another important impact in this regard. Although weather cannot be controlled, the knowledge of how weather affects the air traffic at an airport can be very helpful to optimize air traffic management. Data mining promises to gain that knowledge. Usually, the very first step in data mining is data visualization. In this paper we discuss two new visualization techniques that allow to visualize aviation data and weather data in order to contribute to the optimization process. These modern multi-dimensional scaling techniques provide mappings of high- dimensional data to low-dimensional feature spaces. We will show some results on a practical application of a major European airport.},
doi = {10.1109/nafips.2007.383804},
url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4271027}
}
@inproceedings{RueGebDetKru2004,
author = {Gebhardt, J{\"o}rg
and R{\"u}gheimer, Frank
and Detmer, Heinz
and Kruse, Rudolf},
title = {Adaptable Markov Models in Industrial Planning},
year = {2004},
publisher = {IEEE Press},
url = {http://fuzzy.cs.uni-magdeburg.de/~ruegheim/publications/fieee_04.pdf}
}
@inproceedings{ruegheim2008ipmu,
author = {R{\"u}gheimer, Frank
and Kruse, Rudolf},
editor = {Verdegay, Jos{\'e} Luis
and Magdalena, Luis
and Ojeda-Aciego, Manuel},
title = {An Uncertainty Representation for Set-Valued Attributes with Hierarchical Domains},
year = {2008},
address = {M{\'a}laga},
pages = {197--203},
keywords = {Information Fusion},
keywords = {Knowledge Representation},
keywords = {Random Sets},
abstract = {In collected data information about a single property may be presented with variable resolution and focus. The present paper describes how hierarchically structured attribute domains support the transfer of knowledge between alternative frames of discernment allowing to flexibly serve information needs and facilitate the processing of inhomogeneous data. The approach is later extended to accommodate setvalued attributes, which have previously been employed to represent imprecision and have recently gained attention in text processing, hierarchy learning or multi-label classification.}
}
@inproceedings{RueKru2005,
author = {R{\"u}gheimer, Frank
and Kruse, Rudolf},
title = {Information Miner -- a Data Analysis Platform},
year = {2005},
publisher = {Universitat Polit{\`e}nica de Catalunya},
url = {http://fuzzy.cs.uni-magdeburg.de/~ruegheim/publications/eusflat05.pdf}
}
@inproceedings{RueKru2005b,
author = {R{\"u}gheimer, Frank
and Kruse, Rudolf},
editor = {Mikut, Ralf
and Reischl, Markus},
title = {Datenanalyse-Plattform InformationMiner},
year = {2005},
publisher = {Universit{\"a}tsverlag Karlsruhe},
note = {in German},
url = {http://fuzzy.cs.uni-magdeburg.de/~ruegheim/publications/bomm_05.pdf}
}
@inproceedings{RueNauKru,
author = {R{\"u}gheimer, Frank
and Nauck, Detlef
and Kruse, Rudolf},
editor = {Beyerer, J.
and Le{\'o}n, F. Puente
and Sommer, K.-D.},
title = {Informationsfusion in Neuro-Fuzzy-Systemen},
year = {2006},
publisher = {Universit{\"a}tsverlag Karlsruhe},
pages = {113--125},
note = {in German},
isbn = {3-86644-053-7}
}
@inproceedings{russ2005tencon,
author = {Ru{\ss}, Georg
and Hsu, Arthur L.
and Islam, Aminul
and Halgamuge, Saman K.
and Kruse, Rudolf
and Smith, Alan J.
and Karim, Md A.},
title = {Detection of Faulty Semiconductor Wafers using Dynamic Growing Self Organizing Map},
year = {2006},
publisher = {Piscataway},
address = {New Jersey},
pages = {761--766},
abstract = {Solving product yield and quality problems in a manufacturing process is becoming increasingly more difficult. There are various types of failures and their causes have complex multi-factor interrelationships. Semiconductor manufacturing is very complex due to the large number of processes, diverse equipment set and nonlinear process flows. Its manufacturing database comprises of hundreds of process control, process step and wafer probe data. This huge volume of data coupled with quicker time to market expectations is making finding and resolving problems quickly an overwhelming task. In this study, a methodology developed using dynamic growing self-organizing map (GSOM) to detect the faulty products in a wafer manufacturing process. As part of the methodology, a clustering quality measure was developed to evaluate the performance of the algorithm in separating good and faulty products. Results show that the algorithm was able to separate good and faulty products from the raw data. Even though this work has focused mainly on clustering good and faulty products, the technique can be extended to model the failure causes of the lower yielding products.},
doi = {10.1109/tencon.2005.301056},
url = {http://ieeexplore.ieee.org/search/srchabstract.jsp?tp=&arnumber=4085264}
}
@inproceedings{russ2007nafips,
author = {Ru{\ss}, Georg
and B{\"o}ttcher, Mirko
and Kruse, Rudolf},
title = {Relevance Feedback for Association Rules using Fuzzy Score Aggregation},
year = {2007},
pages = {54--59},
abstract = {We propose a novel and more flexible relevance feedback for association rules which is based on a fuzzy notion of relevance. Our approach transforms association rules into a vector-based representation using some inspiration from document vectors in information retrieval. These vectors are used as the basis for a relevance feedback approach which builds a knowledge base of rules previously rated as (un)interesting by a user. Given an association rule the vector representation is used to obtain a fuzzy score of how much this rule contradicts a rule in the knowledge base. This yields a set of relevance scores for each assessed rule which still need to be aggregated. Rather than relying on a certain aggregation measure we utilize OWA operators for score aggregation to gain a high degree of flexibility and understandability.},
doi = {10.1109/nafips.2007.383810},
url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?tp=&arnumber=4271033&isnumber=4271017}
}
@inproceedings{russ2007relfeedback,
author = {Ru{\ss}, Georg
and Kruse, Rudolf
and Nauck, Detlef
and B{\"o}ttcher, Mirko},
editor = {Bramer, Max},
title = {Relevance Feedback for Association Rules by Leveraging Concepts from Information Retrieval},
series = {Proceedings of AI-2007},
year = {2008},
publisher = {Springer},
address = {Cambridge},
volume = {24},
pages = {253--266},
abstract = {The task of detecting those association rules which are interesting within the vast set of discovered ones still is a major research challenge in data mining. Although several possible solutions have been proposed, they usually require a user to be aware what he knows, to have a rough idea what he is looking for, and to be able to specify this knowledge in advance. In this paper we compare the task of finding the most relevant rules with the task of finding the most relevant documents known from Information Retrieval. We propose a novel and flexible method of rel- evance feedback for association rules which leverages technologies from Information Retrieval, like document vectors, term frequencies and simi- larity calculations. By acquiring a user's preferences our approach builds a repository of what he considers to be (non-)relevant. By calculating and aggregating the similarities of each unexamined rule with the rules in the repository we obtain a relevance score which better reflects the user's notion of relevance with each feedback provided.},
doi = {10.1007/978-1-84800-094-0_19},
url = {http://www.springerlink.com/content/v214143743450884/}
}
@inproceedings{russ2008icdm,
author = {Ru{\ss}, Georg
and Kruse, Rudolf
and Wagner, Peter
and Schneider, Martin},
editor = {Perner, Petra},
title = {Data Mining with Neural Networks for Wheat Yield Prediction},
series = {LNAI},
year = {2008},
publisher = {Springer Verlag},
address = {Leipzig},
volume = {5077},
pages = {47--56},
keywords = {Data Mining},
keywords = {Neural Networks},
keywords = {Precision Agriculture},
keywords = {Prediction},
abstract = {Precision agriculture (PA) and information technology (IT) are closely interwoven. The former usually refers to the application of nowadays' technology to agriculture. Due to the use of sensors and GPS technology, in today's agriculture many data are collected. Making use of those data via IT often leads to dramatic improvements in efficiency. For this purpose, the challenge is to change these raw data into useful information. In this paper we deal with neural networks and their usage in mining these data. Our particular focus is whether neural networks can be used for predicting wheat yield from cheaply-available in-season data. Once this prediction is possible, the industrial application is quite straightforward: use data mining with neural networks for, e.g., optimizing fertilizer usage, in economic or environmental terms.},
isbn = {978-3-540-70717-2},
issn = {0302-9743},
doi = {10.1007/978-3-540-70720-2_4}
}
@inproceedings{russ2008ifip,
author = {Ru{\ss}, Georg
and Kruse, Rudolf
and Schneider, Martin
and Wagner, Peter},
editor = {Bramer, Max},
title = {Estimation of Neural Network Parameters for Wheat Yield Prediction},
series = {IFIP International Federation for Information Processing},
year = {2008},
publisher = {Springer Boston},
volume = {276},
pages = {109--118},
abstract = {Precision agriculture (PA) and information technology (IT) are closely interwoven. The former usually refers to the application of nowadays' technology to agriculture. Due to the use of sensors and GPS technology, in today's agriculture many data are collected. Making use of those data via IT often leads to dramatic improvements in efficiency. For this purpose, the challenge is to change these raw data into useful information. This paper deals with suitable modeling techniques for those agricultural data where the objective is to uncover the existing patterns. In particular, the use of feed-forward backpropagation neural networks will be evaluated and suitable parameters will be estimated. In consequence, yield prediction is enabled based on cheaply available site data. Based on this prediction, economic or environmental optimization of, e.g., fertilization can be carried out.},
doi = {10.1007/978-0-387-09695-7}
}
@inproceedings{russ2008ipmu,
author = {Ru{\ss}, Georg
and Kruse, Rudolf
and Schneider, Martin
and Wagner, Peter},
editor = {Verdegay, Jos{\'e} Luis
and Ojeda-Aciego, Manuel
and Magdalena, Luis},
title = {Optimizing Wheat Yield Prediction Using Different Topologies of Neural Networks},
year = {2008},
address = {M{\'a}laga},
pages = {576--582},
keywords = {Data Mining},
keywords = {Neural Networks},
keywords = {Precision Agriculture},
keywords = {Prediction},
abstract = {Precision agriculture (PA) and information technology (IT) are closely interwoven. The former usually refers to the application of nowadays' technology to agriculture. Due to the use of sensors and GPS technology, in today's agriculture many data are collected. Making use of those data via IT often leads to dramatic improvements in efficiency. For this purpose, the challenge is to change these raw data into useful information by using decision rules. These rules include the management know-how for (economic) optimal recommendations. This paper deals with suitable modeling techniques for those agricultural data where the objective is to uncover the existing patterns. In consequence, yield prediction is enabled based on cheaply available site data. Based on this prediction, economic or environmental optimization of, e.g., fertilization can be carried out.}
}
@inproceedings{russ2009icdm,
author = {Ru{\ss}, Georg},
editor = {Perner, Petra},
title = {Data Mining of Agricultural Yield Data: A Comparison of Regression Models},
series = {LNAI},
year = {2009},
publisher = {Springer},
address = {Berlin, Heidelberg},
volume = {5633},
pages = {24--37},
keywords = {Data Mining},
keywords = {Modeling},
keywords = {Precision Agriculture},
keywords = {Regression},
abstract = {Nowadays, precision agriculture refers to the application of state-of-the- art GPS technology in connection with small-scale, sensor-based treatment of the crop. This introduces large amounts of data which are collected and stored for later usage. Making appropriate use of these data often leads to considerable gains in efficiency and therefore economic advantages. However, the amount of data poses a data mining problem -- which should be solved using data mining techniques. One of the tasks that remains to be solved is yield prediction based on available data. From a data mining perspective, this can be formulated and treated as a multi-dimensional regression task. This paper deals with appropriate regression techniques and evaluates four different techniques on selected agriculture data. A recommendation for a certain technique is provided.},
isbn = {978-3-642-03066-6},
issn = {0302-0743},
doi = {10.1007/978-3-642-03067-3_3},
url = {http://www.springerlink.com/content/3x41838425115j72/}
}
@inproceedings{russ2009mldm,
author = {Ru{\ss}, Georg
and Kruse, Rudolf
and Schneider, Martin
and Wagner, Peter},
editor = {Perner, Petra},
title = {Visual Data Mining of Agriculture Data},
series = {Poster Proceedings},
year = {2009},
publisher = {IBaI publishing},
address = {Leipzig, Germany},
pages = {30--44},
abstract = {Precision agriculture (PA) and information technology (IT) are closely interwoven. The former usually refers to the application of nowadays' technology to agriculture. Due to the use of sensors and GPS technology, in today's agriculture many data are collected. Making use of those data via IT often leads to dramatic improvements in efficiency. For this purpose, the challenge is to change these raw data into useful information. Techniques or methods are required which use those data to their full extent -- clearly being a data mining task. This paper presents experimental results on real and recent agriculture data that aid in the first part of the data mining process: understanding and visualizing the data. Self-organizing maps and multidimensional scaling techniques will be used to reduce the high-dimensional input data to two dimensions. The processed data can then be visualized appropriately on 2D maps. An analysis of correlations and interdependencies in the data set will be given, based on the visualization.},
isbn = {978-3-940501-04-2},
issn = {1864-9734}
}
@inproceedings{russ2009sgai,
author = {Ru{\ss}, Georg
and Kruse, Rudolf
and Schneider, Martin
and Wagner, Peter},
editor = {Allen, Tony
and Ellis, Richard
and Petridis, Miltos},
title = {Visualization of Agriculture Data Using Self-Organizing Maps},
series = {Proceedings of AI-2008},
year = {2009},
publisher = {Springer},
address = {London},
volume = {16},
pages = {47--60},
abstract = {The importance of carrying out effective and sustainable agriculture is getting more and more obvious. In the past, additional fallow ground could be tilled to raise production. Nevertheless, even in industrialized countries agriculture can still improve on its overall yield. Modern technology, such as GPS-based tractors and sensor-aided fertilization, enables farmers to optimize their use of resources, economically and ecologically. However, these modern technologies create heaps of data that are not as easy to grasp and to evaluate as they have once been. Therefore, techniques or methods are required which use those data to their full capacity -- clearly being a data mining task. This paper presents some experimental results on real agriculture data that aid in the first part of the data mining process: understanding and visualizing the data. We present interesting conclusions concerning fertilization strategies which result from data mining.},
isbn = {978-1-84882-214-6},
url = {http://www.springer.com/computer/artificial/book/978-1-84882-214-6}
}
@inproceedings{russ2010icdmregression,
author = {Ru{\ss}, Georg
and Kruse, Rudolf},
editor = {Perner, Petra},
title = {Regression Models for Spatial Data: An Example from Precision Agriculture},
series = {LNAI},
year = {2010},
publisher = {Springer},
volume = {6171},
pages = {450--463},
isbn = {978-3-642-14399-1},
doi = {10.1007/978-3-642-14400-4_35},
url = {http://www.springerlink.com/content/d08m000208j00hl5/}
}
@inproceedings{russ2010icpaaclusteringapproach,
author = {Ru{\ss}, Georg
and Kruse, Rudolf
and Schneider, Martin},
editor = {Khosla, Rajiv},
title = {A Clustering Approach for Management Zone Delineation in Precision Agriculture},
year = {2010},
publisher = {International Society of Precision Agriculture}
}
@inproceedings{russ2010ifcs,
author = {Ru{\ss}, Georg
and Kruse, Rudolf
and Schneider, Martin
and Wagner, Peter},
editor = {Locarek-Junge, Hermann
and Weihs, Claus},
title = {Using Advanced Regression Models for Determining Optimal Soil Heterogeneity Indicators},
series = {Studies in Classification, Data Analysis, and Knowledge Organization},
year = {2010},
publisher = {Springer},
address = {Dresden},
pages = {463--471},
note = {doi: http://dx.doi.org/10.1007/978-3-642-10745-0\_50},
url = {http://www.springerlink.com/content/x8167x2p65392243/}
}
@inproceedings{russ2010ipmu,
author = {Ru{\ss}, Georg
and Brenning, Alexander},
editor = {H{\"u}llermeier, Eyke
and Kruse, Rudolf
and Hoffmann, Frank},
title = {Data Mining in Precision Agriculture: Management of Spatial Information},
series = {LNAI},
year = {2010},
publisher = {Springer},
volume = {6178},
pages = {350--359},
doi = {10.1007/978-3-642-14049-5_36}
}
@inproceedings{russ2010sgai,
author = {Ru{\ss}, Georg
and Kruse, Rudolf},
editor = {Allen, Tony
and Ellis, Richard
and Petridis, Miltos},
title = {Feature Selection for Wheat Yield Prediction},
series = {Proceedings of AI-2009},
year = {2010},
publisher = {Springer},
volume = {26},
pages = {465--478},
keywords = {Data Mining},
keywords = {Feature Selection},
keywords = {Precision Agriculture},
abstract = {Carrying out effective and sustainable agriculture has become an important issue in recent years. Agricultural production has to keep up with an ever-increasing population by taking advantage of a field's heterogeneity. Nowadays, modern technology such as the global positioning system (GPS) and a multitude of developed sensors enable farmers to better measure their fields' heterogeneities. For this small-scale, precise treatment the term precision agriculture has been coined. However, the large amounts of data that are (literally) harvested during the growing season have to be analysed. In particular, the farmer is interested in knowing whether a newly developed heterogeneity sensor is potentially advantageous or not. Since the sensor data are readily available, this issue should be seen from an artificial intelligence perspective. There it can be treated as a feature selection problem. The additional task of yield prediction can be treated as a multi-dimensional regression problem. This article aims to present an approach towards solving these two practically important problems using artificial intelligence and data mining ideas and methodologies.},
isbn = {978-1-84882-982-4},
doi = {10.1007/978-1-84882-983-1_36},
url = {http://www.springerlink.com/content/j27614612tn44664}
}
@inproceedings{russ2011icdm,
author = {Ru{\ss}, Georg
and Kruse, Rudolf},
editor = {Perner, Petra},
title = {Exploratory Hierarchical Clustering for Management Zone Delineation in Precision Agriculture},
series = {LNAI},
year = {2011},
publisher = {Springer},
volume = {NA},
pages = {na--na},
note = {to appear}
}
@inproceedings{russ2011scai,
author = {Ru{\ss}, Georg
and Kruse, Rudolf},
title = {Machine Learning Methods for Spatial Clustering on Precision Agriculture Data},
year = {2011},
publisher = {IOS Press},
pages = {NA--NA},
note = {to appear}
}
@inproceedings{S.1997,
author = {Siekmann, S.
and Kruse, Rudolf
and Neuneier, R.},
title = {Advanced Neuro-Fuzzy Techniques Applied To The German Stock Index DAX},
year = {1997}
}
@inproceedings{S.1997a,
author = {Siekmann, S.
and Kruse, Rudolf
and Neuneier, R.},
title = {T{\"a}gliche Prognose des Deutschen Aktienindex DAX mit Neuro-Fuzzy Methoden},
year = {1997},
pages = {7--18}
}
@inproceedings{S.1997b,
author = {Siekmann, S.
and Kruse, Rudolf
and Neuneier, R.},
title = {Neuro-Fuzzy in der Finanzanalyse. In Tagungsband des 2.Workshops Neuronale Netze in Ingenieursanwendungen},
year = {1997},
publisher = {Universit{\"a}t Stuttgart},
pages = {67--78}
}
@inproceedings{S.1999b,
author = {Siekmann, S.
and Kruse, Rudolf
and Gebhardt, J{\"o}rg
and van Overbeek, F.
and Cooke, R.},
title = {Information Fusion in the Context of Stock Index Prediction},
year = {1999},
publisher = {Springer}
}
@inproceedings{Schroder1995,
author = {Schr{\"o}der, M.
and Kruse, Rudolf},
title = {Sequential Optimization of Characteristic Mappings by Means of Genetic Algorithms},
year = {1995}
}
@inproceedings{Smets1993,
author = {Smets, Philippe
and Kruse, Rudolf},
title = {The Transferable Belief Model for Belief Representation},
year = {1993}
}
@inproceedings{Spiliopoulou_et_al_2005,
editor = {Spiliopoulou, Myra
and Kruse, Rudolf
and Borgelt, Christian
and N{\"u}rnberger, Andreas
and Gaul, Wolfgang},
title = {From Data and Information Analysis to Knowledge Engineering --- Proc.{\backslash} 29th Ann.{\backslash} Conf.{\backslash} of the German Classification Society (GfKl 2005, Magdeburg, Germany)},
year = {2005},
publisher = {Springer-Verlag},
abstract = {The volume contains revised versions of selected papers presented during the 29th Annual Conference of the Gesellschaft f{\"u}r Klassifikation (GfKl), the German Classification Society, held at the Otto-von-Guericke-University of Magdeburg, Germany, in March 2005. In addition to papers on the traditional subjects Classification, Clustering, and Data Analysis, there are many papers on a wide range of topics with a strong relation to Computer Science. Examples are Text Mining, Web Mining, Fuzzy Data Analysis, IT Security, Adaptivity and Personalization, and Visualization. Application-oriented topics include Economics, Marketing, Banking and Finance, Medicine, Bioinformatics, Biostatistics, and Music Analysis.},
isbn = {978-3-540-31313-7},
url = {http://www.springer.com/statistics/book/978-3-540-31313-7}
}
@inproceedings{steinbrecher2008nafips,
author = {Steinbrecher, Matthias
and Kruse, Rudolf},
title = {Identifying Temporal Trajectories of Association Rules with Fuzzy Descriptions},
year = {2008},
address = {New York City, NY},
pages = {1--6},
abstract = {We propose a novel postprocessing technique for identifying sets of association rules that expose a user-specified temporal development. We explicitly do not use a learning approach that requires the database to be subdivided into time frames. Instead, a global probabilistic learning method is used for induction. The resulting association rules are then matched against a set of fuzzy concepts. These concepts comprise user-built linguistic propositions that describe the evolution of rules that might be considered interesting. The proposed technique is evaluated on a real-world data set. To present the results, we introduce a modified rule visualization along the way that is an extension of our previous work.},
isbn = {978-1-4244-2351-4},
doi = {10.1109/nafips.2008.4531243}
}
@inproceedings{steinbrecher2009ifsa,
author = {Steinbrecher, Matthias
and Kruse, Rudolf},
title = {Fuzzy Descriptions to Identify Temporal Substructure Changes of Cooccurrence Graphs},
year = {2009},
pages = {1177--1182},
isbn = {978-989-95079-6-8}
}
@inproceedings{Steinbrecher_Kruse-2006a,
author = {Steinbrecher, Matthias
and Kruse, Rudolf},
editor = {Bello, Rafael
and Falc{\'o}n, Rafael
and G{\'o}mez, Yudel},
title = {Visualization of Local Dependencies of Possibilistic Network Structures},
year = {2006},
pages = {77--80}
}
@inproceedings{Steinbrecher_Kruse-2007a,
author = {Steinbrecher, Matthias
and Kruse, Rudolf},
title = {Visualisierung Bayesscher Netze zur Diagnoseunterst{\"u}tzung},
series = {VDI-Berichte},
year = {2007},
publisher = {VDI-Verlag},
volume = {1980}
}
@inproceedings{Steinbrecher_Kruse-2007c,
author = {Steinbrecher, Matthias
and Kruse, Rudolf},
title = {\{Visualization of Possibilistic Potentials\}},
series = {Lecture Notes in Computer Science},
year = {2007},
publisher = {Springer Berlin / Heidelberg},
volume = {4529},
pages = {295--303},
abstract = {The constantly increasing capabilities of database storage systems leads to an incremental collection of data by business organizations. The research area of Data Mining has become a paramount requirement in order to cope with the acquired information by locating and extracting patterns from these data volumes. Possibilistic networks comprise one prominent Data Mining technique that is capable of encoding dependence and independence relations between variables as well as dealing with imprecision. It will be argued that the learning of the network structure only provides an overview of the qualitative component, yet the more interesting information is contained inside the network parameters, namely the potential tables. In this paper we introduce a new visualization technique that allows for a detailed inspection of the quantitative component of possibilistic networks.},
isbn = {978-3-540-72917-4},
issn = {0302-9743},
doi = {10.1007/978-3-540-72950-1_30}
}
@inproceedings{Steinbrecher_Kruse-2008a,
author = {Steinbrecher, Matthias
and Kruse, Rudolf},
editor = {Bello, Rafael
and Falc{\'o}n, Rafael
and Pedrycz, Witold
and Kacprzyk, J.},
title = {Visualization of Local Dependencies of Possibilistic Network Structures: At the Junction of Rough Sets and Fuzzy Sets},
series = {Studies in Fuzziness and Soft Computing},
year = {2008},
publisher = {Springer Berlin / Heidelberg},
volume = {224},
pages = {93--104},
note = {doi: Visualization of Local Dependencies of Possibilistic Network Structures}
}
@inproceedings{Steinbrecher_Kruse-2008c,
author = {Steinbrecher, Matthias
and Kruse, Rudolf},
editor = {Fink, Andreas
and Lausen, Berthold
and Seidel, Wilfried
and Ultsch, Alfred},
title = {Clustering Association Rules with Fuzzy Concepts},
series = {Studies in Classification, Data Analysis, and Knowledge Organization},
year = {2009},
publisher = {Springer Verlag},
pages = {197--206}
}
@inproceedings{T.1996,
author = {Sutter, T.
and Mollet, G. S.
and Schr{\"o}der, M.
and Kruse, Rudolf
and Gebhardt, J{\"o}rg},
title = {Fuzzy Queries for Top Management Succession Planning},
year = {1996},
pages = {241--246}
}
@inproceedings{timmfuzz02possfuzzclustanal,
author = {Timm, Heiko
and Kruse, Rudolf},
title = {A modification to improve possibilistic fuzzy cluster analysis},
booktitle = {Fuzzy Systems, 2002. FUZZ-IEEE'02. Proceedings of the 2002 IEEE International Conference on},
year = {2002},
volume = {2},
pages = {1460--1465},
abstract = {We explore an approach to possibilistic fuzzy clustering that avoids a severe drawback of the conventional approach, namely that the objective function is truly minimized only if all cluster centers are identical. Our approach is based on the idea that this undesired property can be avoided if we introduce a mutual repulsion of the clusters, so that they are forced away from each other. We develop this approach for the possibilistic fuzzy c-means algorithm and the Gustafson-Kessel algorithm},
doi = {10.1109/fuzz.2002.1006721}
}
@inproceedings{Timm_et_al_2001,
author = {Timm, Heiko
and Borgelt, Christian
and D{\"o}ring, Christian
and Kruse, Rudolf},
title = {Fuzzy Cluster Analysis with Cluster Repulsion},
year = {2001},
publisher = {Verlag Mainz}
}
@inproceedings{Wang_et_al_2005,
author = {Wang, Xiaomeng
and Borgelt, Christian
and Kruse, Rudolf},
title = {Mining Fuzzy Frequent Item Sets},
year = {2005},
publisher = {Tsinghua University Press and Springer-Verlag},
pages = {528--533},
isbn = {7-302-11377-7}
}
@inproceedings{winkler2010clustering,
author = {Winkler, Roland
and Rehm, Frank
and Kruse, Rudolf},
editor = {Fink, Andreas
and Lausen, Berthold
and Seidel, Wilfried
and Ultsch, Alfred},
title = {Clustering with Repulsive Prototypes},
series = {Studies in Classification, Data Analysis, and Knowledge Organization},
year = {2010},
publisher = {Springer Verlag},
pages = {207--215},
abstract = {Although there is no exact definition for the term cluster, in the 2D case, it is fairly easy for human beings to decide which objects belong together. For machines on the other hand, it is hard to determine which objects form a cluster. Depending on the problem, the success of a clustering algorithm depends on the idea of their creators about what a cluster should be. Likewise, each clustering algorithm comprises a characteristic idea of the term cluster. For example the fuzzy c-means algorithm (Kruse et al., Advances in Fuzzy Clustering and Its Applications, Wiley, New York, 2007, pp. 3--30; H{\"o}ppner et al., Fuzzy Clustering, Wiley, Chichester, 1999) tends to find spherical clusters with equal numbers of objects. Noise clustering (Rehm et al., Soft Computing -- A Fusion of Foundations, Methodologies and Applications 11(5):489--494) focuses on finding spherical clusters of user-defined diameter. In this paper, we present an extension to noise clustering that tries to maximize the distances between prototypes. For that purpose, the prototypes behave like repulsive magnets that have an inertia depending on their sum of membership values. Using this repulsive extension, it is possible to prevent that groups of objects are divided into more than one cluster. Due to the repulsion and inertia, we show that it is possible to determine the number and approximate position of clusters in a data set.},
isbn = {978-3-642-01045-3},
issn = {1431-8814},
doi = {10.1007/978-3-642-01044-6_19},
url = {http://www.springerlink.com/content/l747q41ll224v6p1/}
}
@inproceedings{winkler2010clustering-enri,
author = {Winkler, Roland
and Temme, Annette
and B{\"o}sel, Christoph
and Kruse, Rudolf},
title = {Clustering radar tracks to evaluate efficiency indicators},
year = {2010},
publisher = {The Second ENRI International Workshop on ATM/CNS (EIWAC2010)},
pages = {71--94},
url = {http://elib.dlr.de/63908/}
}
@inproceedings{X.2004,
author = {Wang, Xiaomeng
and Nauck, Detlef
and Spott, M.
and Kruse, Rudolf},
title = {Fuzzy Decision Trees - A new CI-Method for the Automatic Data Analysis Platform SPIDA},
year = {2004},
publisher = {Universit{\"a}tsverlag Karlsruhe}
}
@inproceedings{X.2004a,
author = {Wang, Xiaomeng
and Nauck, Detlef
and Spott, M.
and Kruse, Rudolf},
title = {Intelligent Data Analysis with Fuzzy Decision Trees},
year = {2004}
}
@inproceedings{X.2004b,
author = {Wang, Xiaomeng
and Nauck, Detlef
and Spott, M.
and Kruse, Rudolf},
title = {The Fuzzy Decision Tree Module in the Automatic Data Analysis Platform Spida},
year = {2004},
publisher = {Uni G{\"o}ttingen}
}
@inproceedings{DBLP:conf/icaisc/2006,
editor = {Rutkowski, Leszek
and Tadeusiewicz, Ryszard
and Zadeh, Lotfi A.
and Zurada, Jacek M.},
title = {Artificial Intelligence and Soft Computing - ICAISC 2006, 8th International Conference, Zakopane, Poland, June 25-29, 2006, Proceedings},
series = {Lecture Notes in Computer Science},
year = {2006},
publisher = {Springer},
volume = {4029},
isbn = {3-540-35748-3