DocumentCode :
2210996
Title :
Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data
Author :
Müller, Emmanuel ; Günnemann, Stephan ; Färber, Ines ; Seidl, Thomas
Author_Institution :
RWTH Aachen Univ., Aachen, Germany
fYear :
2010
fDate :
13-17 Dec. 2010
Firstpage :
1220
Lastpage :
1220
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 methods. By focusing on open challenges, we try to attract young researchers for participating in this emerging research field.
Keywords :
pattern clustering; grouping object; high dimensional data; multiple clustering solution; multiple interpretation; alternative clustering; data mining; multiple perspectives; orthogonal clustering; subspace clustering;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Mining (ICDM), 2010 IEEE 10th International Conference on
Conference_Location :
Sydney, NSW
ISSN :
1550-4786
Print_ISBN :
978-1-4244-9131-5
Electronic_ISBN :
1550-4786
Type :
conf
DOI :
10.1109/ICDM.2010.85
Filename :
5694114
Link To Document :
بازگشت