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Volumn , Issue , 2002, Pages 600-607

Alternatives to the k-means algorithm that find better clusterings

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; IMAGE SEGMENTATION; LEARNING SYSTEMS; OPTIMIZATION; PATTERN RECOGNITION;

EID: 0038156173     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/584792.584890     Document Type: Conference Paper
Times cited : (394)

References (23)
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    • Some methods for classification and analysis of multivariate observations
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    • MacQueen, J.1
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    • Meǐla, M.1    Heckerman, D.2
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    • X-means: Extending K-means with efficient estimation of the number of clusters
    • Morgan Kaufmann, San Francisco, CA
    • D. Pelleg and A. Moore. X-means: Extending K-means with efficient estimation of the number of clusters. In Proceedings of the 17th International Conf. on Machine Learning, pages 727-734. Morgan Kaufmann, San Francisco, CA, 2000.
    • (2000) Proceedings of the 17th International Conf. on Machine Learning , pp. 727-734
    • Pelleg, D.1    Moore, A.2
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    • An empirical comparison of four initialization methods for the k-means algorithm
    • J. Peña, J. Lozano, and P. Larrañaga. An empirical comparison of four initialization methods for the k-means algorithm. Pattern recognition letters, 20:1027-1040, 1999.
    • (1999) Pattern Recognition Letters , vol.20 , pp. 1027-1040
    • Peña, J.1    Lozano, J.2    Larrañaga, P.3
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    • Fastmix clustering software
    • P. Sand and A. Moore. Fastmix clustering software, 2002. http://www.cs.cmu.edu/-psand/.
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    • Generalized k-harmonic means - Boosting in unsupervised learning
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.