Finding non-redundant, statistically significant regions in high dimensional data: A novel approach to projected and subspace clustering

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2008, Pages 533-541

  Moise, Gabriela ^a   Sander, Jörg ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

Author keywords

Projected clustering; Subspace clustering

Indexed keywords

CLUSTER DENSITIES; COMPUTATIONAL INFEASIBILITIES; DATA SETS; DISJOINT CLUSTERS; EXHAUSTIVE SEARCHES; EXPERIMENTAL EVALUATIONS; HIGH-DIMENSIONAL DATUM; OPTIMIZATION PROBLEMS; OVERLAPPING CLUSTERS; PROBLEM FORMULATIONS; PROJECTED CLUSTERING; SUBSPACE CLUSTERING; USER-DEFINED PARAMETERS; VIABLE SOLUTIONS;

APPROXIMATION ALGORITHMS; DATA MINING; INFORMATION MANAGEMENT; MINING;

CLUSTERING ALGORITHMS;

EID: 65449163900     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1401890.1401956     Document Type: Conference Paper

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