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2013 IEEE 29th International Conference on Data Engineering (ICDE) (2012)
Arlington, Virginia USA
Apr. 1, 2012 to Apr. 5, 2012
ISSN: 1084-4627
ISBN: 978-0-7695-4747-3
pp: 1207-1210
ABSTRACT
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.
INDEX TERMS
data mining, disparate clustering, alternative clustering, subspace clustering, multi-view clustering
CITATION
Ines Färber, Emmanuel Müller, Thomas Seidl, Stephan Günnemann, "Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data", 2013 IEEE 29th International Conference on Data Engineering (ICDE), vol. 00, no. , pp. 1207-1210, 2012, doi:10.1109/ICDE.2012.142
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