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Volumn 7, Issue , 2010, Pages

Application of Compound Gaussian Mixture Model clustering in the data stream

Author keywords

Clustering; Compound Gaussian Mixture Model; Data stream; Semi supervised clustering

Indexed keywords

CLUSTERING; CLUSTERING ANALYSIS; CLUSTERING TECHNOLOGIES; DATA STREAM; DATA STREAM CLUSTERING; DATA STREAM MINING; EM ALGORITHMS; GAUSSIAN MIXTURE MODEL; INITIAL PARAMETER; LABELED DATA; MODELING METHOD; RECOGNITION RATES; SEMI-SUPERVISED CLUSTERING;

EID: 78649571624     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCASM.2010.5620507     Document Type: Conference Paper
Times cited : (2)

References (12)
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  • 3
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  • 4
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    • (2002) Journal of the American Statistical Association , vol.97 , pp. 611-631
    • Fraley, C.1    Raftery, A.E.2
  • 5
    • 78649620101 scopus 로고    scopus 로고
    • Study of EM algorithm on Gaussian mixture model
    • China, November
    • YUE Jia, WANG Shi-tong. Study of EM Algorithm on Gaussian mixture Model.Computer Simulation, China, vol.11, pp. 111-113, November 2007.
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  • 6
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    • Aggarwal, C.1    Han, J.2    Wang, J.3
  • 12
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    • Clustering over multiple evolving streams by events and correlations
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    • Yeh M, Dai B, Chen M.Clustering over Multiple Evolving Streams by Events and Correlations. IEEE Transactions on knowledge and Data Engineering. American, vol. 19, pp. 1349-1362, October2007.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.