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

• Benutzergeleitetes Clustering von Musikdaten (Heinle, Eduard)
• Clustering von Forenbeiträgen zur Erstellung von FAQs (Hennig, Sascha)
• Diagnostik von Telekommunikationsanlagen mit Methoden des maschinellen Lernens (Leibel, Wilhelm)
• Ein Algorithmus zur Lösung des Farthest-Pair-Problems (Stolpe, Marco)
• Entdecken von Themen und Communitys in Online-Foren (Sitta, Andreas)
• Maschinelle Lernverfahren für kollaboratives Tagging (Kaspari, Andreas)
• Outlier Detection in Newsforen (Deutsch, Stephan)
• Visualisierung von Embeddings zur Analyse großer Dokumenten-Kollektionen (Kilian, Phillip)

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.