article.bib

@comment{{This file has been generated by bib2bib 1.96}}

@comment{{Command line: bib2bib -ob article.bib -oc article -c $type="ARTICLE" kruse-all.bib}}

@article{Kruse2010a,
  author = {Kruse, Rudolf
and Steinbrecher, Matthias},
  title = {Visual data analysis with computational intelligence methods},
  journal = {Bulletin of the Polish Academy of Sciences},
  year = {2010},
  volume = {58},
  number = {3},
  issn = {1732-8985(e)}
}

@article{A.1999,
  author = {Hartmann, G. K.
and N{\"u}rnberger, Andreas
and Nauck, Detlef
and Kruse, Rudolf},
  title = {Neuro-Fuzzy Control Based on the NEFCON-Model},
  journal = {Soft Computing},
  year = {1999},
  publisher = {Springer},
  volume = {2},
  number = {4},
  pages = {182--186}
}

@article{A.2001a,
  author = {Appriou, A.
and Ayoun, A.
and Benferhat, S.
and Hartmann, G. K.
and Cooke, R.
and Cuppens, F.
and Dubois, Didier
and Sossai, C.
and N{\"u}rnberger, Andreas
and Klose, Aljoscha
and Kruse, Rudolf},
  title = {Fusion: General Concepts and Characteristics},
  journal = {Intl. J. of Intelligent Systems},
  year = {2001},
  volume = {10},
  pages = {1107--1134}
}

@article{A.2002,
  author = {Hartmann, G. K.
and N{\"u}rnberger, Andreas
and Richards, M.
and Kruse, Rudolf
and Nauck, Detlef},
  title = {A Neuro-Fuzzy Approach to Optimise Hierarchical Recurrent Fuzzy Systems},
  journal = {Fuzzy Optimization and Decision Making},
  year = {2002},
  volume = {1},
  number = {2},
  pages = {221--248}
}

@article{A.2002a,
  author = {Hartmann, G. K.
and N{\"u}rnberger, Andreas
and Klose, Aljoscha
and Kruse, Rudolf
and Richards, M.},
  title = {SomAccess - Ein Softwareprototyp zur interaktiven Navigation in Textdatenbanken},
  journal = {K{\"u}nstliche Intelligenz},
  year = {2002},
  volume = {3},
  number = {2},
  pages = {59--64}
}

@article{Bodenhofer_et_al_editorial_2007,
  author = {Bodenhofer, Ulrich
and H{\"u}llermeier, Eyke
and Klawonn, Frank
and Kruse, Rudolf},
  title = {Special issue on soft computing for information mining [editorial]},
  journal = {Soft Computing: A Fusion of Foundations, Methodologies and Applications},
  year = {2007},
  volume = {11},
  number = {5},
  pages = {397--399},
  issn = {1432-7643},
  doi = {10.1007/s00500-006-0105-3},
  url = {http://springerlink.metapress.com/content/e32k0413521521p7/?p=f8005f795c024d11941f0610f853aae4&pi=0}
}

@article{boettcher2009mining,
  author = {B{\"o}ttcher, Mirko
and Spott, Martin
and Nauck, Detlef
and Kruse, Rudolf},
  title = {Mining changing customer segments in dynamic markets},
  journal = {Expert Systems with Applications},
  year = {2009},
  volume = {36},
  number = {1},
  pages = {155--164},
  doi = {10.1016/j.eswa.2007.09.006}
}

@article{Boettcher_et_al_2006,
  author = {B{\"o}ttcher, Mirko
and Nauck, Detlef
and Borgelt, Christian
and Kruse, Rudolf},
  title = {A Framework for Discovering Interesting Business Changes from Data},
  journal = {BT Technology Journal},
  year = {2006},
  publisher = {British Telecommunications plc},
  volume = {24},
  number = {2},
  pages = {219--228}
}

@article{Borgelt2002,
  author = {Borgelt, Christian
and Eichhorn, A.
and Kruse, Rudolf
and Nauck, Detlef},
  title = {Learning from Imprecise Data: Possibilistic Graphical Models},
  journal = {Computational Statistics and Data Analysis},
  year = {2002},
  publisher = {Elsevier},
  volume = {38},
  pages = {449--463}
}

@article{Borgelt2003,
  author = {Borgelt, Christian
and Lindner, Guido
and Kruse, Rudolf},
  title = {Learning Possibilistic Graphical Models from Data},
  journal = {IEEE Transaction Fuzzy Systems},
  year = {2003},
  publisher = {IEEE Press},
  volume = {11},
  number = {2},
  pages = {159--172}
}

@article{Borgelt_and_Kruse_2001a,
  author = {Borgelt, Christian
and Kruse, Rudolf},
  title = {Unsicherheit und \{V\}agheit: \{B\}egriffe, \{M\}ethoden, \{F\}orschungsthemen},
  journal = {\{K\}{\"u}nstliche Intelligenz, \{T\}hemenheft Unsicherheit und \{V\}agheit},
  year = {2001},
  publisher = {arendtap},
  volume = {15},
  number = {3},
  pages = {5--8}
}

@article{Borgelt_and_Kruse_2003b,
  author = {Borgelt, Christian
and Kruse, Rudolf},
  title = {Operations and Evaluation Measures for Learning Possibilistic Graphical Models},
  journal = {Artificial Intelligence},
  year = {2003},
  publisher = {Elsevier},
  volume = {148},
  pages = {385--418},
  note = {issue=\{1--2\}}
}

@article{C.1998,
  author = {Borgelt, Christian
and Gebhardt, J{\"o}rg
and Kruse, Rudolf
and Nauck, Detlef
and Lindner, Guido},
  title = {Lernen probabilistischer and possibilistischer Netze aus Daten: Theorie und Anwendung},
  journal = {KI-Themenheft Data Mining},
  year = {1998},
  pages = {11--17}
}

@article{Christian2004,
  author = {Borgelt, Christian
and Gebhardt, J{\"o}rg
and Kruse, Rudolf
and Nauck, Detlef},
  title = {Probabilistische graphische Modelle und ihre Anwendung in der Automobilindustrie},
  journal = {Datenbank Spektrum - Zeitschrift f{\"u}r Datenbanktechnologie},
  year = {2004},
  publisher = {dpunkt.verlag},
  volume = {4},
  number = {9}
}

@article{D.1999,
  author = {Nauck, Detlef
and Gebhardt, J{\"o}rg
and Kruse, Rudolf},
  title = {Neuro-Fuzzy Systems for Function Approximation},
  journal = {Fuzzy Sets and Systems},
  year = {1999},
  volume = {101},
  pages = {261--271}
}

@article{D.1999a,
  author = {Nauck, Detlef
and Gebhardt, J{\"o}rg
and Kruse, Rudolf},
  title = {Obtaining Interpretable Fuzzy Classification Rules from Medical Data},
  journal = {Artificial Intelligence in Medicine},
  year = {1999},
  volume = {16},
  pages = {149--169}
}

@article{D{\"o}ring_Lesot_Kruse_DataAnal_2006,
  author = {D{\"o}ring, Christian
and Lesot, Marie-Jeanne
and Kruse, Rudolf},
  title = {Data analysis with fuzzy clustering methods},
  journal = {Computational Statistics {\backslash}\{\&\} Data Analysis},
  year = {2006},
  volume = {51},
  number = {1},
  pages = {192--214},
  note = {available at http://ideas.repec.org/a/eee/csdana/v51y2006i1p192-214.html},
  issn = {0167-9473},
  doi = {10.1016/j.csda.2006.04.030}
}

@article{Eichhorn2002,
  author = {Eichhorn, A.
and Timm, Heiko
and Klose, Aljoscha
and Kruse, Rudolf},
  title = {Soft Computing for Automated Surface Quality analysis of Exterior car body Panels},
  journal = {Soft Computing Journal},
  year = {2002}
}

@article{F.1995,
  author = {D{\"o}ring, Christian
and Klawonn, Frank
and Gebhardt, J{\"o}rg
and Kruse, Rudolf},
  title = {Fuzzy Control on the Basis of Equality Relations with Example from Idle Speed Control},
  journal = {IEEE Transactions on Fuzzy Systems},
  year = {1995},
  volume = {3},
  number = {3},
  pages = {336--356}
}

@article{Gebhardt1993,
  author = {Marx-G{\'o}mez, J.
and Gebhardt, J{\"o}rg
and Kruse, Rudolf},
  title = {The Context Model: An Integrating View of Uncertainty and Vagueness},
  journal = {Int. J. of Approximate Reasoning},
  year = {1993}
}

@article{guenther2009atreatment,
  author = {G{\"u}nther, Tobias
and M{\"u}ller, Iris
and Preuss, Markus
and Kruse, Rudolf
and Sabel, Bernhard},
  title = {A Treatment Outcome Prediction Model of Visual Field Recovery Using Self-Organizing-Maps},
  journal = {IEEE Transactions on Biomedical Engineering},
  year = {2009},
  volume = {56},
  number = {3},
  pages = {572--581},
  abstract = {Brain injuries caused by stroke, trauma or tumor often affect the visual system which leads to perceptual deficits. After intense visual stimulation of the damaged visual field or its border region, recovery may be achieved in some sectors of the visual field, but the extent of restoration is highly variable between patients and it is not homogeneously distributed in the visual field. We now assessed the visual field loss and its dynamics by perimetry, a standard diagnostic procedure in medicine, to measure the detectability of visual stimuli in the visual field. Subsequently, a treatment outcome prediction model (TOPM) was developed, using features which were extracted from the baseline perimetric charts. The features in the TOPM were either empirically associated with treatment outcomes or were based on findings in the vision-restoration literature. Among other classifiers, the Self-Organizing-Map (SOM) was selected because it implicitly supports data exploration. Using a data pool of 52 patients with visual field defects, the TOPM was constructed to predict areas of improvement in the visual-field topography. To evaluate the predictive validity of the TOPM, we propose a method to calculate the Receiver-Operating-Characteristic graph, whereby the SOM is used in combination with a nearest-neighbor classifier. We discuss issues relevant for medical TOPMs, such as appropriateness to the patient sample, clinical relevance, and incorporation of a priori knowledge.},
  doi = {10.1109/TBME.2008.2009995},
  url = {http://www.ncbi.nlm.nih.gov/pubmed/19068421}
}

@article{Heiko2004,
  author = {Marx-G{\'o}mez, J.
and Timm, Heiko
and Borgelt, Christian
and D{\"o}ring, Christian
and Kruse, Rudolf},
  title = {An Extension to Possibilistic Fuzzy Cluster Analysis},
  journal = {Fuzzy Sets and Systems},
  year = {2004},
  volume = {147},
  pages = {3--16}
}

@article{Heiko2004a,
  author = {Timm, Heiko
and Rautenstrauch, C.
and D{\"o}ring, Christian
and Kruse, Rudolf},
  title = {Different approaches to fuzzy clustering of incomplete datasets},
  journal = {International Journal of Approximate Reasoning},
  year = {2004},
  volume = {35},
  pages = {239--249}
}

@article{J.1998b,
  author = {Kruse, Rudolf
and Gebhardt, J{\"o}rg},
  title = {Book-review: Expert systems and probabilistic network models},
  journal = {Mathematical Methods of Operations Research},
  year = {1998},
  pages = {337--339}
}

@article{J.2002,
  author = {Marx-G{\'o}mez, J.
and Gebhardt, J{\"o}rg
and Rautenstrauch, C.
and N{\"u}rnberger, Andreas
and Kruse, Rudolf},
  title = {Neuro-fuzzy approach to forecast returns of scrapped products to recycling and remanufacturing},
  journal = {Knowledge-Based Systems},
  year = {2002},
  volume = {15},
  number = {2},
  pages = {119--128}
}

@article{Joerg2004,
  author = {Detmer, Heinz
and Gebhardt, J{\"o}rg
and Borgelt, Christian
and Kruse, Rudolf},
  title = {Knowledge Revision in Markov Networks},
  journal = {Mathware {\backslash}\{\&\} Soft Computing},
  year = {2004},
  volume = {11},
  number = {2-3},
  pages = {93--107}
}

@article{kempe2008miningfrequent,
  author = {Kempe, Steffen
and Hipp, Jochen
and Lanquillon, Carsten
and Kruse, Rudolf},
  title = {Mining frequent temporal patterns in interval sequences},
  journal = {International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems (IJUFKS)},
  year = {2008},
  volume = {16},
  number = {5},
  pages = {645--661},
  doi = {10.1142/S0218488508005546}
}

@article{Klawonn1993,
  author = {Schwecke, E.
and Klawonn, Frank
and Kruse, Rudolf},
  title = {Equality Relations as a Basis for Fuzzy Control},
  journal = {Fuzzy Sets and Systems},
  year = {1993},
  volume = {54},
  pages = {147--156}
}

@article{Klawonn1994,
  author = {Klawonn, Frank
and Kruse, Rudolf},
  title = {A Lukasiewicz Logic Based Prolog},
  journal = {Mathware and Soft Computing},
  year = {1994},
  volume = {1},
  pages = {5--29}
}

@article{Klawonn1997,
  author = {Klose, Aljoscha
and Klawonn, Frank
and Kruse, Rudolf
and Nauck, Detlef},
  title = {Constructing a Fuzzy Controller from Data},
  journal = {Fuzzy Sets and Systems},
  year = {1997},
  volume = {85},
  pages = {177--193}
}

@article{Klose2000,
  author = {Klose, Aljoscha
and Radetzky, A.
and Kruse, Rudolf
and N{\"u}rnberger, Andreas
and Hartmann, G. K.
and Richards, M.},
  title = {Interactive Text Retrieval Based on Document Similarities},
  journal = {Physics and Chemistry of the Earth},
  year = {2000},
  publisher = {Elsevier Science},
  pages = {649--654}
}

@article{klose2004exploratory,
  author = {Kruse, Rudolf
and Klose, Aljoscha},
  title = {Recent advances in exploratory data analysis with neuro-fuzzy methods},
  journal = {Soft Comput.},
  year = {2004},
  volume = {8},
  number = {6},
  pages = {381--382},
  doi = {10.1007/s00500-003-0293-z}
}

@article{Klose2005,
  author = {Klose, Aljoscha
and Kruse, Rudolf},
  title = {Semi-supervised learning in knowledge discovery},
  journal = {Fuzzy Sets and Systems},
  year = {2005},
  volume = {149},
  pages = {209--233},
  doi = {10.1016/j.fss.2004.07.016}
}

@article{kruse08fuzzy,
  author = {Kruse, Rudolf
and Moewes, Christian},
  title = {Fuzzy neural network},
  journal = {Scholarpedia},
  year = {2008},
  volume = {3},
  number = {11},
  pages = {6043},
  keywords = {Fuzzy neural network},
  keywords = {Neuro-fuzzy system},
  url = {http://www.scholarpedia.org/article/Fuzzy_neural_network}
}

@article{Kruse1982,
  author = {Kruse, Rudolf},
  title = {A Note on Additive Fuzzy Measures, Fuzzy Sets and Systems},
  year = {1982},
  volume = {8},
  pages = {219--222}
}

@article{Kruse1982a,
  author = {Kruse, Rudolf},
  title = {On the Construction of Fuzzy Measures},
  journal = {Fuzzy Sets and Systems},
  year = {1982},
  volume = {8},
  pages = {323--327}
}

@article{Kruse1982b,
  author = {Kruse, Rudolf},
  title = {The strong law of large numbers for fuzzy random variables},
  journal = {Information sciences},
  year = {1982},
  volume = {28},
  pages = {233--241}
}

@article{Kruse1983,
  author = {Kruse, Rudolf},
  title = {On the Entropy of Fuzzy Events},
  journal = {Kybernetes},
  year = {1983},
  volume = {12},
  pages = {53--57},
  doi = {10.1108/eb005641}
}

@article{Kruse1983a,
  author = {Kruse, Rudolf},
  title = {Fuzzy Integrals and Conditional Fuzzy Measures},
  journal = {Fuzzy Sets and Systems},
  year = {1983},
  volume = {10},
  pages = {309--313}
}

@article{Kruse1984,
  author = {Kruse, Rudolf},
  title = {Statistical Estimation with Linguistic Data},
  journal = {Information Sciences},
  year = {1984},
  volume = {33},
  pages = {197--207}
}

@article{Kruse1984a,
  author = {Kruse, Rudolf},
  title = {Probabilistische Mengen},
  journal = {Wiss. Abh. der Braunschweigischen wissenschaftlichen Gesellschaft},
  year = {1984},
  volume = {36},
  pages = {7--13}
}

@article{Kruse1987,
  author = {Kruse, Rudolf},
  title = {On the Entropy of Additive Fuzzy Measures},
  journal = {Journal of Mathematical Analysis and Applications},
  year = {1987},
  volume = {122},
  pages = {589--595}
}

@article{Kruse1987a,
  author = {Struckmann, W.
and Nauck, Detlef
and Gebhardt, J{\"o}rg
and Kruse, Rudolf},
  title = {On the Variance of Random Sets},
  journal = {Journal of Mathematical Analysis and Applications},
  year = {1987},
  volume = {122},
  pages = {469--473}
}

@article{Kruse1987b,
  author = {Kruse, Rudolf},
  title = {On a Software Tool for Statistics with Linguistic Data},
  journal = {Fuzzy Sets and Systems},
  year = {1987},
  volume = {24},
  pages = {377--383}
}

@article{Kruse1990,
  author = {Kruse, Rudolf
and Schwecke, E.},
  title = {Fuzzy Reasoning in a Multidimensional Space of Hypotheses},
  journal = {Int. Journal of Approximate Reasoning},
  year = {1990},
  volume = {4},
  pages = {47--68}
}

@article{Kruse1990a,
  author = {Kruse, Rudolf
and Schwecke, E.},
  title = {Specialization, A New Concept for Uncertainty Handling with Belief Function},
  journal = {International Journal of General Systems},
  year = {1990},
  volume = {18},
  number = {1},
  pages = {49--58}
}

@article{Kruse1991b,
  author = {Kruse, Rudolf},
  title = {ESPRIT-Projekt},
  journal = {KI, DRUMS Defeasible Reasoning and Uncertainty Management Systems},
  year = {1991},
  volume = {4}
}

@article{Kruse1993e,
  author = {Kruse, Rudolf},
  title = {Fuzzy Logic at the University of Braunschweig},
  journal = {La Lettre Logique Floue},
  year = {1993},
  volume = {4}
}

@article{Kruse1993f,
  author = {Kruse, Rudolf},
  title = {Fuzzy-Systeme: Einige Klarstellungen},
  journal = {KI 4, Sparte Pro und Kontra},
  year = {1993}
}

@article{Kruse1996,
  author = {Radetzky, A.
and Kruse, Rudolf},
  title = {Fuzzy-Systeme - Positive Aspekte der Unvollkommenheit},
  journal = {Informatik Spektrum},
  year = {1996},
  volume = {19},
  number = {1},
  pages = {4--11}
}

@article{Kruse1998a,
  author = {Kruse, Rudolf},
  title = {Neuro-Fuzzy-Systeme in der Datenanalyse},
  journal = {Magdeburger Wissenschaftsjournal},
  year = {1999},
  volume = {2}
}

@article{Kruse1999,
  author = {Radetzky, A.
and Kruse, Rudolf},
  title = {Neuronale Fuzzy-Systeme in der Datenanalyse},
  journal = {Magdeburger Wissenschaftsjournal},
  year = {1999},
  volume = {2}
}

@article{kruse2010computsystsci,
  author = {Belohlavek, Radim
and Kruse, Rudolf},
  title = {Editor's foreword},
  journal = {Computer and System Sciences},
  year = {2010},
  volume = {76},
  number = {1},
  pages = {1--2}
}

@article{kruseetal95a,
  author = {Kruse, Rudolf
and Nauck, Detlef
and Klawonn, Frank},
  title = {Neuronale Fuzzy--Systeme},
  journal = {Spektrum der Wissenschaft},
  year = {1995},
  volume = {Juli},
  number = {Heft 6/1995},
  pages = {34--41}
}

@article{Kruse_and_Borgelt_2003,
  author = {Kruse, Rudolf
and Borgelt, Christian},
  title = {Information Mining: Editorial},
  journal = {Int.{\backslash} Journal of Approximate Reasoning},
  year = {2003},
  publisher = {Elsevier},
  volume = {32},
  pages = {63--65}
}

@article{Michels1997,
  author = {Kruse, Rudolf
and Michels, K.},
  title = {Numerical Stability Analysis for Fuzzy Control},
  journal = {International Journal of Approximate Reasoning},
  year = {1997}
}

@article{moewes08zuordnen,
  author = {Moewes, Christian
and Kruse, Rudolf},
  editor = {Rei{\ss}enweber, Bernd},
  title = {Zuordnen von linguistischen \{A\}usdr{\"u}cken zu \{M\}otiven in \{Z\}eitreihen},
  journal = {at-Automatisierungstechnik},
  year = {2009},
  volume = {57},
  number = {3},
  pages = {146--154},
  keywords = {Frequent Pattern Mining},
  keywords = {Labeling},
  keywords = {Motif Discovery},
  keywords = {Multivariate Time Series Analysis},
  abstract = {In diesem Aufsatz geht es um die Problematik, von Experten entworfenen Versuche realen Beobachtungen anzun{\"a}hern.  Dazu wird dieses Problem in zwei kleinere Teilprobleme zerlegt:  1.) die Suche von wiederkehrenden Mustern in temporalen Sequenzen, sogenannten Motiven, die es gilt sowohl in den Versuchen als auch in der Realit{\"a}t zu entdecken und 2.) das Zuordnen der Motive zu linguistischen Ausdr{\"u}cken, die als Dom{\"a}nenwissen eventuell vorhanden sind.  Dabei wird eine effektive Repr{\"a}sentation von Zeitreihen beschrieben, welche die Suche nach diesen Motiven enorm beschleunigt.  Einige Ans{\"a}tze werden dargestellt, um mit den gefundenen Motiven die entworfenen Versuche zu korrigieren.  Abschlie{\ss}end wird die vorliegende Arbeit zusammengefasst und es wird ein Ausblick auf m{\"o}gliche Erweiterungen gegeben.},
  doi = {10.1524/auto.2009.0760}
}

@article{nauck92c,
  author = {Nauck, Detlef
and Klawonn, Frank
and Kruse, Rudolf},
  title = {Fuzzy Sets, Fuzzy Controllers, and Neural Networks},
  journal = {Wissenschaftliche Zeitschrift der Humboldt-Universit{\"a}t zu Berlin, R. Medizin},
  year = {1992},
  volume = {41},
  number = {4},
  pages = {99--120}
}

@article{Nurnberger2001,
  author = {Kruse, Rudolf
and N{\"u}rnberger, Andreas
and Radetzky, A.
and Gebhardt, J{\"o}rg},
  title = {Using Recurrent Neuro-Fuzzy Techniques for the Identification and Simulation of Dynamic Systems},
  journal = {Neurocomputing},
  year = {2001},
  volume = {36},
  pages = {123--147}
}

@article{Nusser2009at,
  author = {Nusser, Sebastian
and Otte, Clemens
and Hauptmann, Werner
and Leirich, Oskar
and Kr{\"a}tschmer, Manfred
and Kruse, Rudolf},
  title = {Maschinelles Lernen von validierbaren Klassifikatoren zur autonomen Steuerung sicherheitsrelevanter Systeme},
  journal = {at -- Automatisierungstechnik},
  year = {2009},
  publisher = {Oldenbourg Verlag},
  volume = {57},
  number = {3},
  pages = {138--145},
  note = {in German},
  doi = {10.1524/auto.2009.0761}
}

@article{P.1985,
  author = {Friedrich, P.
and Kruse, Rudolf
and Struckmann, W.},
  title = {DV-Profis und Studenten bilden Softwareteam},
  journal = {Computerwoche},
  year = {1985}
}

@article{P.1986,
  author = {Kruse, Rudolf
and Friedrich, P.
and Schr{\"o}der, M.
and Struckmann, W.},
  title = {Ein softwaretechnisches Praktikum: Kooperation zwischen Hochschule und Industrie},
  journal = {Angewandte Informatik},
  year = {1986},
  volume = {3},
  pages = {124--126}
}

@article{R.1987,
  author = {Kruse, Rudolf
and Buck-Emden, R.
and Cordes, R.},
  title = {Processor Power Considerations - an Application of Fuzzy Markov Chains},
  journal = {Fuzzy Sets and Systems},
  year = {1987},
  volume = {21},
  pages = {289--299}
}

@article{R.1988c,
  author = {Kruse, Rudolf
and Struckmann, W.
and Friedrich, P.},
  title = {Ein Modell zur Integration von praxisnahen Softwareprojekten in die Informatikausbildung},
  journal = {Handbuch der modernen Datenverarbeitung},
  year = {1988}
}

@article{R.1989,
  author = {Kruse, Rudolf
and Struckmann, W.
and Schwecke, E.},
  title = {{\"U}ber ein integriertes Informationssystem f{\"u}r die Landwirtschaft},
  journal = {Mitteilungen der TU Braunschweig},
  year = {1989},
  volume = {24}
}

@article{R.1991,
  author = {Kruse, Rudolf
and Cooke, R.
and Gebhardt, J{\"o}rg
and Klawonn, Frank},
  title = {Modellierung von Vagheit und Unsicherheit- Fuzzy Logic und andere Kalk{\"u}le},
  journal = {KI},
  year = {1991},
  volume = {4}
}

@article{R.1996,
  author = {Gebhardt, J{\"o}rg
and Kruse, Rudolf},
  title = {Automated Construction of Possibilistic Networks from Data},
  journal = {Journal of Applied Mathematics and Computer Science},
  year = {1996},
  volume = {3},
  number = {6},
  pages = {101--136}
}

@article{R.1997a,
  author = {Kruse, Rudolf
and Klawonn, Frank
and Nauck, Detlef},
  title = {Erlernen von Fuzzy-Regeln},
  journal = {Informatik, Forschung und Entwicklung},
  year = {1997},
  volume = {12},
  number = {1},
  pages = {2--6}
}

@article{R.1997b,
  author = {Kruse, Rudolf
and Siekmann, S.
and Nauck, Detlef
and Klawonn, Frank},
  title = {Neuronale Fuzzy-Systeme},
  journal = {Spektrum der Wissenschaft},
  year = {1997},
  number = {4},
  pages = {92--99}
}

@article{R.1997c,
  author = {Nauck, Detlef
and Cordes, R.
and Kruse, Rudolf},
  title = {A neuro-fuzzy method to learn fuzzy classification rules from data},
  journal = {Fuzzy Sets and Systems},
  year = {1997},
  volume = {89},
  pages = {277--288}
}

@article{R.1998,
  author = {Nauck, Detlef
and Buck-Emden, R.
and Kruse, Rudolf},
  title = {NEFCLASS-X - a soft computing tool to build readable fuzzy classifiers},
  journal = {BT Technology Journal},
  year = {1998},
  volume = {16},
  number = {3},
  pages = {180--190}
}

@article{R.1999,
  author = {Kruse, Rudolf
and Nauck, Detlef
and Schr{\"o}der, M.},
  title = {Neuro-fuzzy systems for function approximation, Fuzzy Sets and Systems},
  year = {1999},
  volume = {101},
  pages = {261--271}
}

@article{rehmetal2006polarmap,
  author = {Rehm, Frank
and Klawonn, Frank
and Kruse, Rudolf},
  title = {POLARMAP - Efficient Visualisation of High Dimensional Data},
  journal = {Proceedings of the conference on Information Visualization},
  year = {2006},
  publisher = {IEEE Computer Society},
  pages = {731--740},
  issn = {1550-6037},
  doi = {10.1109/iv.2006.85}
}

@article{Rehm_etal_2007_FuzzyClassifiers,
  author = {Rehm, Frank
and Klawonn, Frank
and Kruse, Rudolf},
  title = {Visualization of Fuzzy Classifiers},
  journal = {International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems (IJUFKS)},
  year = {2007},
  volume = {15},
  number = {5},
  pages = {615--624},
  keywords = {Air Traffic Management},
  keywords = {Fuzzy classifiers},
  keywords = {Fuzzy Rules},
  keywords = {multidimensional scaling},
  keywords = {Visualization},
  abstract = {This paper presents different techniques to visualize high-dimensional fuzzy rule bases in relation to the classified data. The degree of membership to influential rules can be visualized for an entire data set. This enables the observer to detect conflicting or error-prone rules as well as misclassified feature vectors. Results are shown on a benchmark data set and on a weather data set that is used to predict flight durations on a major European airport.},
  doi = {10.1142/s021848850700490x}
}

@article{Rehm_et_al_noise_clustering_2007,
  author = {Rehm, Frank
and Klawonn, Frank
and Kruse, Rudolf},
  title = {A novel approach to noise clustering for outlier detection},
  journal = {Soft Computing - A Fusion of Foundations, Methodologies and Applications},
  year = {2007},
  volume = {11},
  number = {5},
  pages = {489--494},
  keywords = {Fuzzy clustering},
  keywords = {Noise clustering},
  keywords = {Outlier detection},
  abstract = {Noise clustering, as a robust clustering method, performs partitioning of data sets reducing errors caused by outliers. Noise clustering defines outliers in terms of a certain distance, which is called noise distance. The probability or membership degree of data points belonging to the noise cluster increases with their distance to regular clusters. The main purpose of noise clustering is to reduce the influence of outliers on the regular clusters. The emphasis is not put on exactly identifying outliers. However, in many applications outliers contain important information and their correct identification is crucial. In this paper we present a method to estimate the noise distance in noise clustering based on the preservation of the hypervolume of the feature space. Our examples will demonstrate the efficiency of this approach.},
  issn = {1432-7643},
  doi = {10.1007/s00500-006-0112-4},
  url = {http://springerlink.metapress.com/content/3310528547m1w712/?p=f8005f795c024d11941f0610f853aae4&pi=7}
}

@article{rieger2008sciencedirect,
  author = {Rieger, Jochem W.
and Reichert, Christoph
and Gegenfurtner, Karl R.
and Noesselt, Toemme
and Braun, Christoph
and Heinze, Hans-Jochen
and Kruse, Rudolf
and Hinrichs, Hermann},
  title = {Predicting the recognition of natural scenes from single trial MEG recordings of brain activity},
  journal = {NeuroImage},
  year = {2008},
  volume = {42},
  number = {3},
  pages = {1056--1068},
  abstract = {In our daily life we look at many scenes. Some are rapidly forgotten, but others we recognize later. We accurately predicted recognition success with natural scene photographs using single trial magnetoencephalography (MEG) measures of brain activation. Specifically, we demonstrate that MEG responses in the initial 600?ms following the onset of scene photographs allow for prediction accuracy rates up to 84.1{\backslash}\% using linear Support-Vector-Machine classification (lSVM). A permutation test confirmed that all lSVM based prediction rates were significantly better than ``guessing''. More generally, we present four approaches to analyzing brain function using lSVMs. (1) We show that lSVMs can be used to extract spatio-temporal patterns of brain activation from MEG-data. (2) We show lSVM classification can demonstrate significant correlations between comparatively early and late processes predictive of scene recognition, indicating dependencies between these processes over time. (3) We use lSVM classification to compare the information content of oscillatory and event-related MEG-activations and show they contain a similar amount of and largely overlapping information. (4) A more detailed analysis of single-trial predictiveness of different frequency bands revealed that theta band activity around 5?Hz allowed for highest prediction rates, and these rates are indistinguishable from those obtained with a full dataset. In sum our results clearly demonstrate that lSVMs can reliably predict natural scene recognition from single trial MEG-activation measures and can be a useful tool for analyzing predictive brain function.},
  doi = {10.1016/j.neuroimage.2008.06.014}
}

@article{Siekmann2001,
  author = {Siekmann, S.
and Kruse, Rudolf
and van Overbeek, F.
and Cooke, R.
and Gebhardt, J{\"o}rg},
  title = {Information Fusion in the Context of Stock Index Prediction},
  journal = {International Journal of Intelligent Systems},
  year = {2001},
  volume = {11},
  pages = {1285--1298}
}

@article{Steinbrecher2009jcss,
  author = {Steinbrecher, Matthias
and Kruse, Rudolf},
  title = {Visualizing and fuzzy filtering for discovering temporal trajectories of association rules},
  journal = {Journal of Computer and System Sciences},
  year = {2010},
  volume = {76},
  number = {1},
  pages = {77--87},
  keywords = {Linguistic description},
  issn = {0022-0000},
  doi = {10.1016/j.jcss.2009.05.007},
  url = {http://www.sciencedirect.com/science/article/B6WJ0-4W9XBK8-5/2/741ff4b112734f19c2bc9df34204e453}
}

@article{timm2002fuzzyclusteranalysis,
  author = {Timm, Heiko
and D{\"o}ring, Christian
and Kruse, Rudolf},
  title = {Fuzzy Cluster Analysis of Partially Missing Data Sets},
  journal = {Second International Workshop on Hybrid Methods for Adaptive Systems I},
  year = {2002},
  pages = {426--431},
  abstract = {A common problem in data analysis are missing attribute values in datasets. The easiest way to handle such datasets in fuzzy cluster analysis is to discard data with missing values. Since this complete case approach may result in a loss of valuable information and reduced dataset size, we study how missing values can be handled by modified fuzzy clustering methods. These approaches are based on iterated imputation of missing values, available case estimation of the cluster parameters, and the introduction of a class specific probability for missing values. Benchmark datasets with randomly deleted attribute values are used to demonstrate the capability of the presented approaches. Our experiments show that the modified clustering methods are superior to a complete case analysis.}
}

@article{Timm_et_al_2004,
  author = {Timm, Heiko
and Borgelt, Christian
and D{\"o}ring, Christian
and Kruse, Rudolf},
  title = {An Extension to Possibilistic Fuzzy Cluster Analysis},
  journal = {Fuzzy Sets and Systems},
  year = {2004},
  publisher = {Elsevier},
  volume = {147},
  pages = {3--16},
  note = {issue=\{1\}}
}

@article{Wang_et_al_fuzzy_decision_trees_2007,
  author = {Wang, Xiaomeng
and Nauck, Detlef
and Spott, Martin
and Kruse, Rudolf},
  title = {Intelligent data analysis with fuzzy decision trees},
  journal = {Soft Computing: A Fusion of Foundations, Methodologies and Applications},
  year = {2007},
  volume = {11},
  number = {5},
  pages = {439--457},
  keywords = {Classification models},
  keywords = {Fuzzy decision trees},
  keywords = {Fuzzy rule learning},
  keywords = {Intelligent data analysis},
  abstract = {Intelligent data analysis has gained increasing attention in business and industry environments. Many applications are looking not only for solutions that can automate and de-skill the data analysis process, but also methods that can deal with vague information and deliver comprehensible models. Under this consideration, we present an automatic data analysis platform, in particular, we investigate fuzzy decision trees as a method of intelligent data analysis for classification problems. We present the whole process from fuzzy tree learning, missing value handling to fuzzy rules generation and pruning. To select the test attributes of fuzzy trees we use a generalized Shannon entropy. We discuss the problems connected with this generalization arising from fuzzy logic and propose some amendments. We give a theoretical comparison on the fuzzy rules learned by fuzzy decision trees with some other methods, and compare our classifiers to other well-known classification methods based on experimental results. Moreover, we show a real-world application for the quality control of car surfaces using our approach.},
  issn = {1432-7643},
  doi = {10.1007/s00500-006-0108-0},
  url = {http://springerlink.metapress.com/content/47h42h58g96x7087/?p=f8005f795c024d11941f0610f853aae4&pi=3}
}

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