Hauptnavigation

Clustering

The aim of cluster analysis is to group objects according to their similarity. Beside traditional cluster analysis there are many current challenges concerning semi-supervised clustering or co-clustering. Incremental clustering for interactive applications is an important current research topic.

Software

Subspace Clustering Extension
YALE Clustering Plugin (now fully integrated in RapidMiner)

Staff

Hess, Sibylle
Morik, Katharina
Stolpe, Marco

Past Master Thesis

Publications

Bohnen/etal/2013a Bohnen, Fabian and Stolpe, Marco and Deuse, Jochen and Morik, Katharina. Using a Clustering Approach with Evolutionary Optimized Attribute Weights to Form Product Families for Production Leveling. In Windt, Katja (editors), Robust Manufacturing Control, pages 189--202, Berlin, Heidelberg, Springer, 2013.
Bohnen/etal/2013b Bohnen, Fabian and Buhl, Matthias and Deuse, Jochen. Systematic Procedure for leveling of low volume and high mix production. In CIRP Journal od Manufacturing Science and Technology, Vol. 6, No. 1, pages 53-58, 2013.
Kaspari/2007a Kaspari, Andreas. Maschinelle Lernverfahren für kollaboratives Tagging. 2007.
Kaspari/Wurst/2007a Kaspari, Andreas and Wurst, Michael. Multi-objective Frequent Termset Clustering. In Proceedings of the Workshop on Knowledge Discovery, Data Mining, and Machine Learning (KDML), 2007.
Morik/Wurst/2007a Morik, Katharina and Wurst, Michael. Multi-Aspect Tagging for Collaborative Structuring. In Michael R. Berthold and Katharina Morik and Arno Siebes (editors), Parallel Universes and Local Patterns, No. 07181, Internationales Begegnungs- und Forschungszentrum für Informatik (IBFI), Schloss Dagstuhl, Germany, 2007.
Deutsch/2006a Deutsch, Stephan. Outlier Detection in USENET Newsgruppen. University of Dortmund, 2006.
Hennig/Wurst/2006a Hennig, Sascha and Wurst, Michael. Incremental Clustering of Newsgroup Articles. In Moonis Ali and Richard Dapoigny (editors), Proceedings of the International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE 06), pages 332--341, Berlin, Heidelberg, Springer, 2006.
Mierswa/Wurst/2006a Mierswa, Ingo and Wurst, Michael. Information Preserving Multi-Objective Feature Selection for Unsupervised Learning. In Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos Coello Coello and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens (editors), GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pages 1545--1552, New York, NY, USA, ACM Press, 2006.
Mierswa/Wurst/2006b Mierswa, Ingo and Wurst, Michael. Sound Multi-Objective Feature Space Transformation for Clustering. In Proceedings of the Knowledge Discovery, Data Mining, and Machine Learning (KDML), pages 330--337, 2006.
Morik/Rueping/2006a Morik, Katharina and Rüping, Stefan. Klassifikations-/Clustermethoden und Konjunkturanalyse. Duncker & Humbolt, 2006.
Morik/Rueping/2006b Morik, Katharina and Rüping, Stefan. An Inductive Logic Programming Approach to the Classification of Phases in Business Cycles. In Heilemann, U. and Weihs, Claus (editors), Classification and Clustering in Business Cycle Analysis, Duncker & Humbolt, 2006.
Wurst/etal/2006a Wurst, Michael and Morik, Katharina and Mierswa, Ingo. Localized Alternative Cluster Ensembles for Collaborative Structuring. In Johannes Fürnkranz and Tobias Scheffer and Myra Spiliopoulou (editors), Proceedings of the European Conference on Machine Learning, pages 485--496, Berlin, Springer, 2006.
Heinle/2005a Heinle, Eduard. Benutzergeleitetes Clustering von Musikdaten. Fachbereich Informatik, Universit\"at Dortmund, 2005.
Homburg/etal/2005a Homburg, Helge and Mierswa,Ingo and Moller, Bulent and Morik, Katharina and Wurst, Michael. A Benchmark Dataset for Audio Classification and Clustering. In Joshua D. Reiss and Geraint A. Wiggins (editors), Proc. of the International Symposium on Music Information Retrieval 2005, pages 528--531, London, UK, Queen Mary University, 2005.
Wurst/etal/2005a Wurst, Michael and Mierswa, Ingo and Morik, Katharina. Structuring Music Collections by Exploiting Peers' Processing. No. 43/05, Collaborative Research Center 475, University of Dortmund, 2005.
Stolpe/2003a Stolpe, Marco. Ein Algorithmus zur Losung des Farthest-Pair-Problems. Universitat Dortmund, 2003.
Schewe/97b Schewe, Sandra. Automatische Kategorisierung von Volltexten unter Anwendung von NLP-Techniken. Fachbereich Informatik, Universitat Dortmund, 1997.
Morik/Kietz/89b Morik, Katharina and Kietz, Jörg-Uwe. A Bootstrapping Approach to Conceptual Clustering. In Proc. Sixth Intern. Workshop on Machine Learning, 1989.