SCALE: A scalable framework for efficiently clustering transactional data

Data Mining and Knowledge Discovery

Volumn 20, Issue 1, 2010, Pages 1-27

  Yan, Hua ^a   Chen, Keke ^b   Liu, Ling ^c   Yi, Zhang ^d  

^a UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b Wright State University   (United States)

^c Georgia Institute of Technology   (United States)

^d SICHUAN UNIVERSITY   (China)

Author keywords

Cluster assessment; Cluster validation; Frequent itemset mining; Transactional data clustering; Weighted coverage density

Indexed keywords

CLUSTER VALIDATION; CLUSTER VALIDITY; CLUSTERING ANALYSIS; CLUSTERING EVALUATION; CLUSTERING RESULTS; DATA SETS; DENSITY MEASURES; DOMAIN SPECIFIC; EXPERIMENTAL EVALUATION; FREQUENT ITEMSET MINING; HIGH QUALITY; REAL DATA SETS; SAMPLE DATASET; SCALABLE CLUSTERING; SELF-CONFIGURING; SIMILARITY MEASURE; TRANSACTIONAL DATA; TWO DOMAINS; UNIQUE FEATURES;

CLUSTER ANALYSIS; SCALES (WEIGHING INSTRUMENTS);

CLUSTERING ALGORITHMS;

EID: 76749108949     PISSN: 13845810     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10618-009-0134-5     Document Type: Article

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