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Volumn 19, Issue 9, 2008, Pages 2339-2348

Local density based distributed clustering algorithm

Author keywords

Density attractor; Distributed clustering; High dimension data; Local clustering model; Local density based clustering

Indexed keywords

CLUSTER ANALYSIS; ELECTRIC NETWORK ANALYSIS; FLOW OF SOLIDS;

EID: 52349117708     PISSN: 10009825     EISSN: None     Source Type: Journal    
DOI: 10.3724/SP.J.1001.2008.02339     Document Type: Article
Times cited : (17)

References (8)
  • 2
    • 84949487685 scopus 로고    scopus 로고
    • A scalable parallel subspace clustering algorithm for massive data sets
    • Nagesh HS, Goil S, Choudhary A. A scalable parallel subspace clustering algorithm for massive data sets. In: Proc. of the 2000 Int'l Conf. on Parallel. 2000. 477-484. http://citeseer.ist.psu.edu/377365.html
    • (2000) Proc. of the 2000 Int'l Conf. on Parallel , pp. 477-484
    • Nagesh, H.S.1    Goil, S.2    Choudhary, A.3
  • 5
    • 85170282443 scopus 로고    scopus 로고
    • A density-based algorithm for discovering clusters in large spatial databases with noise
    • Simoudis E., Han J. and Fayyad U.M. (ed.), Oregon: AAAI Press
    • Ester M, Kriegel HP, Sander J, Xu X. A density-based algorithm for discovering clusters in large spatial databases with noise. In: Simoudis E, Han J, Fayyad UM, eds. Proc. of the 2nd Int'l Conf. on Knowledge Discovery and Data Mining. Oregon: AAAI Press, 1996. 226-231.
    • (1996) Proc. of the 2nd Int'l Conf. on Knowledge Discovery and Data Mining , pp. 226-231
    • Ester, M.1    Kriegel, H.P.2    Sander, J.3    Xu, X.4
  • 6
    • 20144374840 scopus 로고    scopus 로고
    • K-LDCHD: A local density based k-neighborhood clustering algorithm for high dime nsional space
    • in Chinese
    • Ni WW, Sun ZH, Lu JP. K-LDCHD: A local density based k-neighborhood clustering algorithm for high dime nsional space. Journal of Computer Research and Development, 2005, 42(5): 784-791 (in Chinese with English abstract).
    • (2005) Journal of Computer Research and Development , vol.42 , Issue.5 , pp. 784-791
    • Ni, W.W.1    Sun, Z.H.2    Lu, J.P.3
  • 8
    • 14944348667 scopus 로고    scopus 로고
    • Clustering validity assessment: Finding the optimal partitioning of a data set
    • Halkidi M, Vazirgiannis M. Clustering validity assessment: Finding the optimal partitioning of a data set. In: Proc. of the 1st IEEE Int'l Conf. on Data Mining. 187-194. http://citeseer.ist.psu.edu/519636.html
    • Proc. of the 1st IEEE Int'l Conf. on Data Mining , pp. 187-194
    • Halkidi, M.1    Vazirgiannis, M.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.