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Volumn 29, Issue 8, 2008, Pages 846-850

An incremental irregular grid algorithm for clustering data streams

Author keywords

Clustering; Data mining; Data stream; Grid

Indexed keywords

ALGORITHMS; CHLORINE COMPOUNDS; CLUSTER ANALYSIS; DATA MINING; FLOW OF SOLIDS; RIVERS;

EID: 52349085948     PISSN: 10067043     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (1)

References (10)
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  • 3
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  • 4
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    • O'Callaghan, L.1    Mishra, N.2    Meyerson, A.3
  • 5
    • 85012236181 scopus 로고    scopus 로고
    • A frame-work for clustering evolving data streams
    • Berlin, Germany
    • Aggarwal C, Han J, Wang J, et al. A frame-work for clustering evolving data streams[C]// VLDB 2003. Berlin, Germany, 2003: 81-92.
    • (2003) VLDB 2003 , pp. 81-92
    • Aggarwal, C.1    Han, J.2    Wang, J.3
  • 6
    • 33646727422 scopus 로고    scopus 로고
    • Arbitrary shape cluster algorithm for clustering data stream
    • Zhu Weihuan, Yin Jian, Xie Yihuang. Arbitrary shape cluster algorithm for clustering data stream [J]. Journal of Software, 2006, 17(3): 379-387.
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    • Zhu, W.1    Yin, J.2    Xie, Y.3
  • 8
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    • Automatic subspace clustering of high dimensional data for data mining applications
    • Washington D.C., USA
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    • Agrawal, R.1    Johannes, G.2    Dimitrios, G.3
  • 9
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    • Approximate frequency counts over data streams
    • Hong Kong, China
    • Manku G S, Motwani R. Approximate frequency counts over data streams [C]// VLDB 2002. Hong Kong, China, 2002: 346-357.
    • (2002) VLDB 2002 , pp. 346-357
    • Manku, G.S.1    Motwani, R.2
  • 10
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    • A framework for projected clustering of high dimensional data streams
    • San Francisco: Morgan Kaufmann
    • Aggarwal C, Han J, Wang J, et al. A framework for projected clustering of high dimensional data streams[C]// Proc 30th Intl Conf Very Large Data Bases. San Francisco: Morgan Kaufmann, 2004: 852-863.
    • (2004) Proc 30th Intl Conf Very Large Data Bases , pp. 852-863
    • Aggarwal, C.1    Han, J.2    Wang, J.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.