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Volumn 218, Issue 7, 2004, Pages 783-794

An incremental K-means algorithm

Author keywords

Clustering; Incremental clustering; K means method

Indexed keywords

DATA ACQUISITION; IMAGE PROCESSING; IMAGE SEGMENTATION; PROBLEM SOLVING;

EID: 3242768576     PISSN: 09544062     EISSN: None     Source Type: Journal    
DOI: 10.1243/0954406041319509     Document Type: Article
Times cited : (78)

References (31)
  • 3
    • 0031069945 scopus 로고    scopus 로고
    • The LBG-U method for vector quantization - An improvement over LBG inspired from neural networks
    • Fritzke, B. The LBG-U method for vector quantization-an improvement over LBG inspired from neural networks. Neural Processing Letters 5, No. 1, 1997, pp. 35-45.
    • (1997) Neural Processing Letters , vol.5 , Issue.1 , pp. 35-45
    • Fritzke, B.1
  • 6
    • 0000981892 scopus 로고    scopus 로고
    • A contiguity-enhanced K-means clustering algorithm for unsupervised multispectral image segmentation
    • Theiler, J. and Gisler, G. A contiguity-enhanced K-means clustering algorithm for unsupervised multispectral image segmentation. Proc. SPIE, 1997, 3159, 108-118.
    • (1997) Proc. SPIE , vol.3159 , pp. 108-118
    • Theiler, J.1    Gisler, G.2
  • 10
    • 3242775358 scopus 로고
    • Utterance clustering for large vocabulary continuous speech recognition
    • Madrid, Spain
    • Cook, G. D. and Robinson, A. J. Utterance clustering for large vocabulary continuous speech recognition. In Proceedings of the European Conference on Speech Technology, Madrid, Spain, Vol. 1, 1995, pp. 219-222.
    • (1995) Proceedings of the European Conference on Speech Technology , vol.1 , pp. 219-222
    • Cook, G.D.1    Robinson, A.J.2
  • 12
    • 0037116773 scopus 로고    scopus 로고
    • Simultaneous grouping of parts and machines with an integrated fuzzy clustering method
    • Josien, K. and Liao, T. W. Simultaneous grouping of parts and machines with an integrated fuzzy clustering method. Fuzzy Sets and Systems, 2002, 126(l), 1-21.
    • (2002) Fuzzy Sets and Systems , vol.126 , Issue.1 , pp. 1-21
    • Josien, K.1    Liao, T.W.2
  • 13
    • 0037116779 scopus 로고    scopus 로고
    • Modified fuzzy C-means algorithm for cellular manufacturing
    • Lozano, S., Dobado, D., Larraneta, J. and Onieva, L. Modified fuzzy C-means algorithm for cellular manufacturing. Fuzzy Sets and Systems, 2002, 126(l), 23-32.
    • (2002) Fuzzy Sets and Systems , vol.126 , Issue.1 , pp. 23-32
    • Lozano, S.1    Dobado, D.2    Larraneta, J.3    Onieva, L.4
  • 14
    • 0001457509 scopus 로고
    • Some methods for classification and analysis of multivariate observations
    • Berkeley, California, (University of California Press, Los Angeles, California)
    • MacQueen, J. B. Some methods for classification and analysis of multivariate observations. In Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, Statistics, Berkeley, California, 1967, pp. 281-297 (University of California Press, Los Angeles, California).
    • (1967) Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, Statistics , vol.1 , pp. 281-297
    • MacQueen, J.B.1
  • 15
    • 0002163773 scopus 로고
    • Convergence properties of the K-means algorithm
    • MIT Press, Cambridge, Massachusetts
    • Bottou, L. and Bengio, Y. Convergence properties of the K-means algorithm. In Advances in Neural Information Processing Systems, Vol. 7, 1995 (MIT Press, Cambridge, Massachusetts).
    • (1995) Advances in Neural Information Processing Systems , vol.7
    • Bottou, L.1    Bengio, Y.2
  • 16
    • 0033204902 scopus 로고    scopus 로고
    • An empirical comparison of four initialisation methods for the K-means algorithm
    • Pena, J. M., Lazano, J. A. and Larranaga, P. An empirical comparison of four initialisation methods for the K-means algorithm. Pattern Recognition Lett., 1999, 20, 1027-1040.
    • (1999) Pattern Recognition Lett. , vol.20 , pp. 1027-1040
    • Pena, J.M.1    Lazano, J.A.2    Larranaga, P.3
  • 22
    • 0036825879 scopus 로고    scopus 로고
    • The effect of finite sample size on on-line K-means
    • Bermejo, S. and Cabestany, J. The effect of finite sample size on on-line K-means. Neurocomputing, 2002, 48, 511-539.
    • (2002) Neurocomputing , vol.48 , pp. 511-539
    • Bermejo, S.1    Cabestany, J.2
  • 26
    • 0034815902 scopus 로고    scopus 로고
    • The enhanced LBG algorithm
    • Patane, G. and Russo, M. The enhanced LBG algorithm. Neural Networks, 2001, 14, 1219-1237.
    • (2001) Neural Networks , vol.14 , pp. 1219-1237
    • Patane, G.1    Russo, M.2
  • 27
    • 0029196051 scopus 로고
    • Optimal adaptive K-means algorithm with dynamic adjustment of learning rate
    • January
    • Chinrungrueng, C. and Sequin, C. H. Optimal adaptive K-means algorithm with dynamic adjustment of learning rate. IEEE Trans. Neural Networks, January 1995, 6(l), 157-169.
    • (1995) IEEE Trans. Neural Networks , vol.6 , Issue.1 , pp. 157-169
    • Chinrungrueng, C.1    Sequin, C.H.2
  • 28
  • 31
    • 0003408496 scopus 로고    scopus 로고
    • Department of Information and Computer Science, University of California Irvine, California
    • Blake, C., Keogh, E. and Merz, C. J. UCI Repository of machine learning databases. Department of Information and Computer Science, University of California Irvine, California, 1998.
    • (1998) UCI Repository of Machine Learning Databases
    • Blake, C.1    Keogh, E.2    Merz, C.J.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.