Efficient mining of maximal frequent itemsets from databases on a cluster of workstations

Knowledge and Information Systems

Volumn 16, Issue 3, 2008, Pages 359-391

  Chung, Soon M ^a   Luo, Congnan ^a  

^a Wright State University   (United States)

Author keywords

Association rules; Maximal frequent item sets; Parallel data mining; Scalability

Indexed keywords

ASSOCIATION RULES; COMPUTER WORKSTATIONS; DATABASE SYSTEMS; DIGITAL STORAGE; FORESTRY; MINERS; PARALLEL ALGORITHMS; SCALABILITY; TREES (MATHEMATICS);

CLUSTER OF WORKSTATIONS; COMMUNICATION AND SYNCHRONIZATIONS; DISTRIBUTED COMPUTING SYSTEMS; LEVEL-WISE ALGORITHMS; MAXIMAL FREQUENT ITEMSETS; MINING ALGORITHMS; PARALLEL DATA MINING; PERFORMANCE AND SCALABILITIES;

DATA MINING;

EID: 51049084211     PISSN: 02191377     EISSN: 02193116     Source Type: Journal    
DOI: 10.1007/s10115-007-0115-1     Document Type: Article

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