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Volumn , Issue , 2012, Pages 13-17

Constrained clustering with Minkowski Weighted K-Means

Author keywords

Constrained K Means; feature weighting; Minkowski metric; Minkowski Weighted K Means; semi supervised learning

Indexed keywords

FEATURE WEIGHTING; K-MEANS; MINKOWSKI METRIC; SEMI-SUPERVISED LEARNING; WEIGHTED K-MEANS;

EID: 84876898879     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CINTI.2012.6496753     Document Type: Conference Paper
Times cited : (36)

References (17)
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  • 6
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    • Ball, G.H.1    Hall, D.J.2
  • 8
    • 1842762839 scopus 로고    scopus 로고
    • An optimization algorithm for clustering using weighted dissimilarity measures
    • E.Y. Chan et al. "An optimization algorithm for clustering using weighted dissimilarity measures". In: Pattern recognition 37.5 (2004), pp. 943-952.
    • (2004) Pattern Recognition , vol.37 , Issue.5 , pp. 943-952
    • Chan Et Al, E.Y.1
  • 10
    • 80055024879 scopus 로고    scopus 로고
    • Minkowski metric, feature weighting and anomalous cluster initializing in K-means clustering
    • R.C. de Amorim and B. Mirkin. "Minkowski Metric, Feature Weighting and Anomalous Cluster Initializing in K-Means Clustering". In: Pattern Recognition 45.3 (2012), pp. 1061-1075.
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    • De Amorim, R.C.1    Mirkin, B.2
  • 11
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    • Chiang, M.M.T.1    Mirkin, B.2
  • 15
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    • Constrained intelligent K-means: Improving results with limited previous knowledge
    • R.C. de Amorim. "Constrained Intelligent K-Means: Improving Results with Limited Previous Knowledge." In: ADVCOMP'08. IEEE. 2008, pp. 176-180.
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  • 17
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.