A general framework for efficient clustering of large datasets based on activity detection

Statistical Analysis and Data Mining

Volumn 4, Issue 1, 2011, Pages 11-29

  Jin, Xin ^a   Kim, Sangkyum ^a   Han, Jiawei ^a   Cao, Liangliang ^b   Yin, Zhijun ^a  

^a University of Illinois at Urbana Champaign   (United States)

^b UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

Activity detection; Clustering; Hierarchical clustering; K Means; Kd tree

Indexed keywords

APPROXIMATION ALGORITHMS; CLUSTERING ALGORITHMS; DATA MINING;

ACTIVITY DETECTION; CLUSTERINGS; DATA CLUSTERING; DETECTION ALGORITHM; HIER-ARCHICAL CLUSTERING; HIERARCHICAL CLUSTERING; HIGHER EFFICIENCY; K-D TREE; K-MEANS; LARGE DATASETS;

EFFICIENCY;

EID: 79551705053     PISSN: 19321872     EISSN: 19321864     Source Type: Journal    
DOI: 10.1002/sam.10097     Document Type: Article

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