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Volumn 49, Issue 4, 2005, Pages 969-973

Optimising k-means clustering results with standard software packages

Author keywords

Cluster analysis; Criterion optimisation; Iterative refinement; Starting values

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; CONSTRAINT THEORY; HEURISTIC METHODS; OPTIMIZATION; PERTURBATION TECHNIQUES; PROBABILITY; STATISTICAL METHODS;

EID: 19044364181     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2004.06.017     Document Type: Article
Times cited : (45)

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    • A k-means clustering algorithm. Algorithm AS136
    • J.A. Hartigan, and M.A. Wong A k-means clustering algorithm. Algorithm AS136 Appl. Stat. 28 1979 100 108
    • (1979) Appl. Stat. , vol.28 , pp. 100-108
    • Hartigan, J.A.1    Wong, M.A.2
  • 7
    • 0001457509 scopus 로고
    • Some methods for classification and analysis of multivariate observations
    • LeCam, L., Neyman, J. (Eds.) University of California Press, Berkeley
    • MacQueen, J., 1967. Some methods for classification and analysis of multivariate observations. In: LeCam, L., Neyman, J. (Eds.), Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1. University of California Press, Berkeley, pp. 281-297.
    • (1967) Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability , vol.1 , pp. 281-297
    • MacQueen, J.1
  • 8
    • 33847457966 scopus 로고
    • An examination of the effect of six types of error perturbation on fifteen clustering algorithms
    • G.W. Milligan An examination of the effect of six types of error perturbation on fifteen clustering algorithms Psychometrika 45 1980 325 342
    • (1980) Psychometrika , vol.45 , pp. 325-342
    • Milligan, G.W.1
  • 9
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    • An empirical comparison of four initialisation methods for the k-means algorithm
    • J.M. Peña, J.A. Lozano, and P. Larranaga An empirical comparison of four initialisation methods for the k-means algorithm Pattern Recognit. Lett. 20 1999 1027 1040
    • (1999) Pattern Recognit. Lett. , vol.20 , pp. 1027-1040
    • Peña, J.M.1    Lozano, J.A.2    Larranaga, P.3
  • 11
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    • Finding the number of clusters in a dataset: An information-theoretic approach
    • S.A. Sugar, and G.M. James Finding the number of clusters in a dataset an information-theoretic approach J. Amer. Stat. Assoc. 98 2003 750 763
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    • Sugar, S.A.1    James, G.M.2
  • 12
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    • Hierarchical grouping to optimize an objective function
    • J.H. Ward Hierarchical grouping to optimize an objective function J. Amer. Stat. Assoc. 58 1963 236 244
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.