메뉴 건너뛰기




Volumn 51, Issue 1, 2006, Pages 215-234

Extending fuzzy and probabilistic clustering to very large data sets

Author keywords

Clustering; Data mining; Extensibility; Fuzzy c means; Goodness of fit; Progressive sampling; Very large data sets

Indexed keywords

ALGORITHMS; DATA MINING; FUZZY SETS; PROBABILITY; STATISTICAL METHODS; VECTORS;

EID: 33746912228     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2006.02.008     Document Type: Article
Times cited : (100)

References (33)
  • 2
    • 0016542694 scopus 로고
    • Optimal fuzzy partitions: a heuristic for estimating the parameters in a mixture of normal distributions
    • Bezdek J.C., and Dunn J.C. Optimal fuzzy partitions: a heuristic for estimating the parameters in a mixture of normal distributions. IEEE Trans. Comput. 24 8 (1975) 835-838
    • (1975) IEEE Trans. Comput. , vol.24 , Issue.8 , pp. 835-838
    • Bezdek, J.C.1    Dunn, J.C.2
  • 3
    • 0036082940 scopus 로고    scopus 로고
    • Bezdek, J.C., Hathaway, R.J., 2002. VAT: a tool for visual assessment of (cluster) tendency. In: Proceedings of the IJCNN 2002, IEEE Press, Piscataway, NJ, pp. 2225-2230.
  • 6
    • 0001684051 scopus 로고    scopus 로고
    • A geometric approach to cluster validity for normal mixtures
    • Bezdek J.C., Li W.Q., Attikiouzel Y.A., and Windham M.P. A geometric approach to cluster validity for normal mixtures. Soft Comput. 1 (1997) 166-179
    • (1997) Soft Comput. , vol.1 , pp. 166-179
    • Bezdek, J.C.1    Li, W.Q.2    Attikiouzel, Y.A.3    Windham, M.P.4
  • 8
    • 33750321791 scopus 로고    scopus 로고
    • Bradley, P., Fayyad, U., Reina, C., 1998a. Scaling clustering algorithms to large databases, In: Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, AAAI Press, Menlo Park, CA, pp. 9-15.
  • 9
    • 33750346798 scopus 로고    scopus 로고
    • Bradley, P., Fayyad, U., Reina, C., 1998b. Scaling EM (expectation-maximization) clustering to large databases. Technical Report MSR-TR-98-35, Microsoft Research, Redmond, WA.
  • 10
    • 0022682981 scopus 로고
    • Efficient implementation of the fuzzy c-means algorithm
    • Cannon R.L., Dave J.V., and Bezdek J.C. Efficient implementation of the fuzzy c-means algorithm. IEEE Trans. PAMI 8 (1986) 248-255
    • (1986) IEEE Trans. PAMI , vol.8 , pp. 248-255
    • Cannon, R.L.1    Dave, J.V.2    Bezdek, J.C.3
  • 11
    • 0029226150 scopus 로고    scopus 로고
    • Cheng, T.W., Goldgof, D.B., Hall, L.O., 1995. Fast clustering with application to fuzzy rule generation. In: Proceedings of the IEEE International Conference on Fuzzy Systems, Tokyo, Japan, pp. 2289-2295.
  • 12
    • 0026961606 scopus 로고    scopus 로고
    • Cutting, D.R., Karger, D.R., Pederson, J.O., Tukey, J.W., 1992. Scatter/gather: a cluster-based approach to browsing large document collections, In: Proceedings of the ACM SIGIR'92, pp. 318-329.
  • 13
    • 33750366086 scopus 로고    scopus 로고
    • Domingos, P., Hulten, G., 2001. A general method for scaling up machine learning algorithms and its application to clustering. In: Proceedings of the 18th International Conference on Machine Learning, pp. 106-113.
  • 15
    • 33750363267 scopus 로고    scopus 로고
    • Farnstrom, F., Lewis, J., Elkan, C., 2000. Scalability for clustering algorithms revisited. SIGKKD Explorations, vol. 2(1). ACM press, New York, pp. 1-7.
  • 16
    • 33750326734 scopus 로고    scopus 로고
    • Fayyad, U., Smyth, P., 1996. From massive data sets to science catalogs: applications and challenges. In: Kettenring, J., Pregibon, D. (Eds.), Proceedings of the Workshop on Massive Data Sets, National Research Council.
  • 17
    • 0035998835 scopus 로고    scopus 로고
    • Model-based clustering, discriminant analysis, and density estimation
    • Fraley C., and Raftery A.E. Model-based clustering, discriminant analysis, and density estimation. J. Amer. Statist. Assoc. 97 458 (2002) 611-631
    • (2002) J. Amer. Statist. Assoc. , vol.97 , Issue.458 , pp. 611-631
    • Fraley, C.1    Raftery, A.E.2
  • 18
    • 0032630575 scopus 로고    scopus 로고
    • Ganti, V., Gehrke, J., Ramakrishnan, R., 1999a. Mining very large databases, Computer, August, pp. 38-45.
  • 19
    • 85175742375 scopus 로고    scopus 로고
    • Ganti, V., Ramakrishnan, R., Gehrke, J., Powell, A.L., French, J.C., 1999b. Clustering large datasets in arbitrary metric spaces, Proceedings of the 15th International Conference on Data Engineering, IEEE CS Press, Los Alamitos, CA, pp. 502-511.
  • 20
    • 0032091595 scopus 로고    scopus 로고
    • Guha, S., Rastogi, R., Shim, K., 1998. CURE: an efficient clustering algorithm for large databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 73-84.
  • 21
    • 84947668623 scopus 로고
    • On the asymptotic properties of fuzzy c-means cluster prototypes as estimators of mixture subpopulation centers
    • Hathaway R.J., and Bezdek J.C. On the asymptotic properties of fuzzy c-means cluster prototypes as estimators of mixture subpopulation centers. Comm. Statist. (A) 15 2 (1986) 505-513
    • (1986) Comm. Statist. (A) , vol.15 , Issue.2 , pp. 505-513
    • Hathaway, R.J.1    Bezdek, J.C.2
  • 22
    • 0001583858 scopus 로고
    • Estimating the parameters of mixture models with modal estimators
    • Hathaway R.J., Redner R., and Bezdek J.C. Estimating the parameters of mixture models with modal estimators. Comm. Statist. (A) 16 9 (1987) 2639-2660
    • (1987) Comm. Statist. (A) , vol.16 , Issue.9 , pp. 2639-2660
    • Hathaway, R.J.1    Redner, R.2    Bezdek, J.C.3
  • 23
    • 33750328380 scopus 로고    scopus 로고
    • Huber, P., 1996. Massive Data Sets Workshop: The Morning After, Massive Data Sets. National Academy Press, pp. 169-184.
  • 25
    • 0036531325 scopus 로고    scopus 로고
    • Reducing the time complexity of the fuzzy c-means algorithm
    • Kolen J.F., and Hutcheson T. Reducing the time complexity of the fuzzy c-means algorithm. IEEE Trans. Fuzzy Systems 10 (2002) 263-267
    • (2002) IEEE Trans. Fuzzy Systems , vol.10 , pp. 263-267
    • Kolen, J.F.1    Hutcheson, T.2
  • 27
    • 0242698067 scopus 로고    scopus 로고
    • The learning curve sampling method applied to model based clustering
    • Meek C., Thiesson B., and Heckerman D. The learning curve sampling method applied to model based clustering. J. Mach. Learning Res. 2 (2002) 397-418
    • (2002) J. Mach. Learning Res. , vol.2 , pp. 397-418
    • Meek, C.1    Thiesson, B.2    Heckerman, D.3
  • 28
    • 33750355433 scopus 로고    scopus 로고
    • Ng, R.T., Han, J., 1994. Efficient and effective clustering methods for spatial data mining. In: Proceedings of the 20th International Conference on Very Large Databases, Morgan Kauffman, San Francisco, pp. 144-155.
  • 30
    • 33750367243 scopus 로고    scopus 로고
    • Provost, F., Jensen, D., Oates, T., 1999. Efficient progressive sampling, In: Proceedings of the Fifth KDDM, ACM Press, New York, pp. 23-32.
  • 32
    • 33750348587 scopus 로고    scopus 로고
    • Uma Shankar, B., Pal, N.R., 1994. FFCM: an effective approach for large data sets. In: Proceedings of the Third International Conference on Fuzzy Logic, Neural nets, and Soft Computing, IIZUKA, Fukuoka, Japan, pp. 332-332.
  • 33
    • 0030157145 scopus 로고    scopus 로고
    • Zhang, T., Ramakrishnan, R., Livny, M., 1996. BIRCH: an efficient data clustering method for very large databases. Proceedings of the ACM SIGMOD International Conference on Management of Data, ACM Press, New york, pp. 103-114.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.