PROXIMUS: A framework for analyzing very high dimensional discrete-attributed datasets

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

Volumn , Issue , 2003, Pages 147-156

  Koyutürk, Mehmet ^a   Grama, Ananth ^a  

^a Purdue University   (United States)

Author keywords

Compressing discrete valued vectors; Non orthogonal matrix decompositions; Semi discrete decomposition

Indexed keywords

ANALYSIS ALGORITHMS; ASSOCIATION RULE MINING; DATA ANALYSIS; DATA SETS; HIGH-DIMENSIONAL; KEY TECHNOLOGIES; LARGE DATASETS; LOSS OF ACCURACY; NON-ORTHOGONAL MATRIX DECOMPOSITIONS; ORTHOGONAL MATRIX; PRECISION AND RECALL; RUNTIMES; SEMI-DISCRETE DECOMPOSITION; SUPPORT THRESHOLD;

ASSOCIATION RULES; ASSOCIATIVE PROCESSING;

DATA MINING;

EID: 70350644882     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/956750.956770     Document Type: Conference Paper

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