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Volumn 1, Issue , 2009, Pages 169-172

Semi-supervised kernel clustering algorithm based on seed set

Author keywords

Kernel kmeans; Seed; Semi supervised clustering

Indexed keywords

DATA POINTS; FEATURE SPACE; INITIAL CLUSTER CENTERS; K-MEANS; K-MEANS ALGORITHM; KERNEL CLUSTERING; KERNEL KMEANS; KERNEL METHODS; OTHER ALGORITHMS; SEED SET; SEEDING STRATEGIES; SEMI-SUPERVISED; SEMI-SUPERVISED CLUSTERING;

EID: 70649106895     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/APCIP.2009.50     Document Type: Conference Paper
Times cited : (2)

References (10)
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    • MAY
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    • Camastra, F.1    Verri, A.2
  • 6
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    • Maximum Likelihood from Incomplete Data via the EM Algorithm
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    • (1977) J. Royal Statistical Soc , vol.39 , Issue.1 , pp. 1-38
    • Dempster, A.P.1    Laird, N.M.2    Rubin, D.B.3
  • 8
    • 38649085067 scopus 로고    scopus 로고
    • An adaptive fuzzy c-means clustering-based mixtures of experts model for unlabeled data classification
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    • (2008) Neurocomputing , vol.71 , pp. 1008-1021
    • Xing, H.-J.1    Hu, B.-G.2
  • 9
    • 0036565280 scopus 로고    scopus 로고
    • Mark Girolami, Mercer Kernel-Based Clustering in Feature Space. IEEE Trans. on NN, 13, No.3, May 2002.
    • Mark Girolami, Mercer Kernel-Based Clustering in Feature Space. IEEE Trans. on NN, Vol.13, No.3, May 2002.
  • 10
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    • UCI repository of machine learning databases
    • UCI repository of machine learning databases, http://www.ics.uci.edu/ mlearn/MLRepository.html


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.