Discovering multiple clustering solutions: grouping objects in different views of the data

Emmanuel Müller, Stephan Günnemann, Ines Färber, Thomas Seidl

Research output: Contribution to journalConference articlepeer-review

35 Scopus citations


Traditional clustering algorithms identify just a single clustering of the data. Today's complex data, however, allow multiple interpretations leading to several valid groupings hidden in different views of the database. Each of these multiple clustering solutions is valuable and interesting as different perspectives on the same data and several meaningful groupings for each object are given. Especially for high dimensional data, where each object is described by multiple attributes, alternative clusters in different attribute subsets are of major interest. In this tutorial, we describe several real world application scenarios for multiple clustering solutions. We abstract from these scenarios and provide the general challenges in this emerging research area. We describe state-of-the-art paradigms, we highlight specific techniques, and we give an overview of this topic by providing a taxonomy of the existing clustering methods. By focusing on open challenges, we try to attract young researchers for participating in this emerging research field.

Original languageEnglish
Article number6228169
Pages (from-to)1207-1210
Number of pages4
JournalProceedings - International Conference on Data Engineering
StatePublished - 2012
Externally publishedYes
EventIEEE 28th International Conference on Data Engineering, ICDE 2012 - Arlington, VA, United States
Duration: 1 Apr 20125 Apr 2012


  • alternative clustering
  • data mining
  • disparate clustering
  • multi-view clustering
  • subspace clustering


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