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Volumn , Issue , 2007, Pages 1250-1254

Distance based subspace clustering with flexible dimension partitioning

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTER ANALYSIS; CLUSTERING ALGORITHMS; COMPUTER SIMULATION; DATA MINING; GRID COMPUTING;

EID: 34548723854     PISSN: 10844627     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDE.2007.368985     Document Type: Conference Paper
Times cited : (45)

References (20)
  • 2
    • 0039253822 scopus 로고    scopus 로고
    • Finding generalized projected clusters in high dimensional spaces
    • C. C. Aggarwal and P. S. Yu. Finding generalized projected clusters in high dimensional spaces. In Proc. of the 2000 ACM SIGMOD Conference, pages 70-81, 2000.
    • (2000) Proc. of the 2000 ACM SIGMOD Conference , pp. 70-81
    • Aggarwal, C.C.1    Yu, P.S.2
  • 7
    • 0036039291 scopus 로고    scopus 로고
    • A new cell-based clustering method for large, high-dimensional data in data mining applications
    • J.-W. Chang and D.-S. Jin. A new cell-based clustering method for large, high-dimensional data in data mining applications. In Proc. of the 2002 ACM symposium on Applied computing, pages 503-507, 2002.
    • (2002) Proc. of the 2002 ACM symposium on Applied computing , pp. 503-507
    • Chang, J.-W.1    Jin, D.-S.2
  • 10
    • 0033569406 scopus 로고    scopus 로고
    • Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring
    • T. Golub and et al. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science, 286:531-537, 1999.
    • (1999) Science , vol.286 , pp. 531-537
    • Golub, T.1    and et, al.2
  • 13
    • 0242387333 scopus 로고    scopus 로고
    • Mafia: Efficient and scalable subspace clustering for very large data sets
    • Technical Report 9906-010, Northwestern University, June
    • H. Nagesh, S. Goil, and A. Choudhary. Mafia: Efficient and scalable subspace clustering for very large data sets. Technical Report 9906-010, Northwestern University, June 1999.
    • (1999)
    • Nagesh, H.1    Goil, S.2    Choudhary, A.3
  • 15
    • 5444223340 scopus 로고    scopus 로고
    • J. Pei, X. Zhang, M. Cho, H. Wang, and P. S. Yu. Maple: A fast algorithm for maximal pattern-based clustering. In Proc. of the 3rd ICDM Conference, pages 259-266, 2003.
    • J. Pei, X. Zhang, M. Cho, H. Wang, and P. S. Yu. Maple: A fast algorithm for maximal pattern-based clustering. In Proc. of the 3rd ICDM Conference, pages 259-266, 2003.
  • 17
    • 77953564323 scopus 로고    scopus 로고
    • Lcm ver. 3: Collaboration of array, bitmap and prefix tree for frequent itemset mining
    • T. Uno, M. Kiyomi, and H. Arimura. Lcm ver. 3: Collaboration of array, bitmap and prefix tree for frequent itemset mining. In Proc. of the ACM SIGKDD OSDM workshop, 2005.
    • (2005) Proc. of the ACM SIGKDD OSDM workshop
    • Uno, T.1    Kiyomi, M.2    Arimura, H.3
  • 19
    • 0742324835 scopus 로고    scopus 로고
    • Findit: A fast and intelligent subspace clustering algorithm, using dimension voting
    • K.-G. Woo, J.-H. Lee, M.-H. Kim, and Y.-J. Lee. Findit: a fast and intelligent subspace clustering algorithm, using dimension voting. Information & Software Technology, 46(4):255-271, 2004.
    • (2004) Information & Software Technology , vol.46 , Issue.4 , pp. 255-271
    • Woo, K.-G.1    Lee, J.-H.2    Kim, M.-H.3    Lee, Y.-J.4
  • 20
    • 0036211103 scopus 로고    scopus 로고
    • J. Yang, W. Wang, H. Wang, and P. S. Yu. δ-clusters: Capturing subspace correlation in a large data set. In Proc. of the 18th IEEE ICDE Conference, pages 517-528, 2002.
    • J. Yang, W. Wang, H. Wang, and P. S. Yu. δ-clusters: Capturing subspace correlation in a large data set. In Proc. of the 18th IEEE ICDE Conference, pages 517-528, 2002.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.