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Volumn , Issue , 2006, Pages 367-376

Efficiently clustering transactional data with weighted coverage density

Author keywords

AMI; LISR; SCALE; Weighted coverage density

Indexed keywords

DATA STRUCTURES; METRIC SYSTEM; PARAMETER ESTIMATION; PROBLEM SOLVING;

EID: 34547626216     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1183614.1183668     Document Type: Conference Paper
Times cited : (16)

References (24)
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    • C. H. Q. Ding, X. He, H. Zha, M. Gu, and H. D. Simon. A min-max cut algorithm for graph partitioning and data clustering. Proceedings of ICDM 2001,pages 107-114, 2001.
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    • Ding, C.H.Q.1    He, X.2    Zha, H.3    Gu, M.4    Simon, H.D.5
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    • 27144536001 scopus 로고    scopus 로고
    • Extensions to the k-means algorithm for clustering large data sets with categorical values
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    • Huang, Z.1
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    • Clope: A fast and effective clustering algorithm for transactional data
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    • Yang, Y.1    Guan, X.2    You, J.3
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    • Bipartite graph partitioning and data clustering
    • H. Zha, X. He, C H. Q. Ding, M. Gu, and H. D. Simon. Bipartite graph partitioning and data clustering. CIKM,pages 25-32, 2001.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.