DocumentCode :
3105009
Title :
Bregman Bubble Clustering: A Robust, Scalable Framework for Locating Multiple, Dense Regions in Data
Author :
Gupta, Gunjan ; Ghosh, Joydeep
Author_Institution :
Dept. of Electr. & Comput. Eng., Texas Univ., Austin, TX
fYear :
2006
fDate :
18-22 Dec. 2006
Firstpage :
232
Lastpage :
243
Abstract :
In traditional clustering, every data point is assigned to at least one cluster. On the other extreme, one class clustering algorithms proposed recently identify a single dense cluster and consider the rest of the data as irrelevant. However, in many problems, the relevant data forms multiple natural clusters. In this paper, we introduce the notion of Bregman bubbles and propose Bregman bubble clustering (BBC) that seeks k dense Bregman bubbles in the data. We also present a corresponding generative model, soft BBC, and show several connections with Bregman clustering, and with a one class clustering algorithm. Empirical results on various datasets show the effectiveness of our method.
Keywords :
data handling; pattern clustering; Bregman bubble clustering; data dense regions; datasets; scalable framework; Bioinformatics; Clustering algorithms; Data engineering; Euclidean distance; Mass spectroscopy; Partitioning algorithms; Phylogeny; Proteins; Robustness; Unsupervised learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Mining, 2006. ICDM '06. Sixth International Conference on
Conference_Location :
Hong Kong
ISSN :
1550-4786
Print_ISBN :
0-7695-2701-7
Type :
conf
DOI :
10.1109/ICDM.2006.32
Filename :
4053051
Link To Document :
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