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Volumn 51, Issue 1, 2006, Pages 192-214

Data analysis with fuzzy clustering methods

Author keywords

Alternating cluster estimation; Comparison with expectation maximization; Current research; Fuzzy maximum likelihood estimation; Noise and outlier handling; Objective function based methods; Probabilistic and possibilistic cluster partitions

Indexed keywords

ALGORITHMS; DATABASE SYSTEMS; FUZZY SETS; PROBABILITY;

EID: 33750289748     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2006.04.030     Document Type: Article
Times cited : (112)

References (43)
  • 1
    • 0028667333 scopus 로고
    • Clustering with evolutionary strategies
    • Babu G.P., and Murty M.N. Clustering with evolutionary strategies. Pattern Recognition 27 (1994) 321-329
    • (1994) Pattern Recognition , vol.27 , pp. 321-329
    • Babu, G.P.1    Murty, M.N.2
  • 3
    • 33750304794 scopus 로고    scopus 로고
    • Bezdek, J.C., 1973. Fuzzy mathematics in pattern classification. Ph.D. Thesis, Applied Mathematics Center, Cornell University, Ithaca.
  • 7
    • 33750294798 scopus 로고    scopus 로고
    • Blake, C.L., Merz, C.J., 1998. UCI repository of machine learning databases. URL 〈http://www.ics.uci.edu/∼mlearn/MLRepository.html〉.
  • 9
    • 33750367239 scopus 로고    scopus 로고
    • Borgelt, C., Kruse, R., 2005. Fuzzy and probabilistic clustering with shape and size constraints. In: Proceedings of the 11th International Fuzzy Systems Association World Congress, IFSA'05, Beijing, China, pp. 945-950.
  • 10
    • 0038006805 scopus 로고    scopus 로고
    • Three-way fuzzy clustering models for lr fuzzy time trajectories
    • Coppi R., and D'Urso P. Three-way fuzzy clustering models for lr fuzzy time trajectories. Comput. Statist. Data Anal. 43 2 (2003) 149-177
    • (2003) Comput. Statist. Data Anal. , vol.43 , Issue.2 , pp. 149-177
    • Coppi, R.1    D'Urso, P.2
  • 11
    • 27744501380 scopus 로고    scopus 로고
    • Fuzzy unsupervised classification of multivariate time trajectories with the Shannon entropy regularization
    • Coppi R., and D'Urso P. Fuzzy unsupervised classification of multivariate time trajectories with the Shannon entropy regularization. Comput. Statist. Data Anal. 50 6 (2006) 1452-1477
    • (2006) Comput. Statist. Data Anal. , vol.50 , Issue.6 , pp. 1452-1477
    • Coppi, R.1    D'Urso, P.2
  • 12
    • 0000586827 scopus 로고
    • Characterization and detection of noise in clustering
    • Davé R. Characterization and detection of noise in clustering. Pattern Recognition Lett. 12 (1991) 657-664
    • (1991) Pattern Recognition Lett. , vol.12 , pp. 657-664
    • Davé, R.1
  • 16
    • 0002629270 scopus 로고
    • Maximum likelihood estimation from incomplete data via the EM algorithm (with discussion)
    • Dempster A.P., Laird N.M., and Rubin D.B. Maximum likelihood estimation from incomplete data via the EM algorithm (with discussion). J. Roy. Statist. Soc. Ser. B 39 (1977) 1-38
    • (1977) J. Roy. Statist. Soc. Ser. B , vol.39 , pp. 1-38
    • Dempster, A.P.1    Laird, N.M.2    Rubin, D.B.3
  • 17
    • 4544378530 scopus 로고    scopus 로고
    • Döring, C., Borgelt, C., Kruse, R., 2004. Fuzzy clustering of quantitative and qualitative data. In: Dick, S., Kurgan, L., Musilek, P., Pedrycz, W., Reformat M. (Eds.), Proceedings of the 2004 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS), IEEE, Banff, Alberta, Canada, pp. 84-89.
  • 21
    • 0015644825 scopus 로고
    • A fuzzy relative of the isodata process and its use in detecting compact, well separated clusters
    • Dunn J.C. A fuzzy relative of the isodata process and its use in detecting compact, well separated clusters. J. Cybernet. 3 (1974) 95-104
    • (1974) J. Cybernet. , vol.3 , pp. 95-104
    • Dunn, J.C.1
  • 22
    • 27744444692 scopus 로고    scopus 로고
    • A weighted fuzzy c-means clustering model for fuzzy data
    • D'Urso P., and Giordani P. A weighted fuzzy c-means clustering model for fuzzy data. Comput. Statist. Data Anal. 50 6 (2006) 1496-1523
    • (2006) Comput. Statist. Data Anal. , vol.50 , Issue.6 , pp. 1496-1523
    • D'Urso, P.1    Giordani, P.2
  • 24
    • 0000764772 scopus 로고
    • The use of multiple measurements in taxonomic problems
    • Fisher R.A. The use of multiple measurements in taxonomic problems. Ann. Eugenics 7 2 (1936) 179-188
    • (1936) Ann. Eugenics , vol.7 , Issue.2 , pp. 179-188
    • Fisher, R.A.1
  • 25
    • 0030260017 scopus 로고    scopus 로고
    • A robust algorithm for automatic extraction of an unknown number of clusters from noisy data
    • Frigui H., and Krishnapuram R. A robust algorithm for automatic extraction of an unknown number of clusters from noisy data. Pattern Recognition Lett. 17 (1996) 1223-1232
    • (1996) Pattern Recognition Lett. , vol.17 , pp. 1223-1232
    • Frigui, H.1    Krishnapuram, R.2
  • 28
    • 57849144858 scopus 로고
    • Optimization of clustering criteria by reformulation
    • Hathaway R.J., and Bezdek J.C. Optimization of clustering criteria by reformulation. IEEE Trans. Fuzzy Systems 3 (1995) 241-245
    • (1995) IEEE Trans. Fuzzy Systems , vol.3 , pp. 241-245
    • Hathaway, R.J.1    Bezdek, J.C.2
  • 31
    • 35248878870 scopus 로고    scopus 로고
    • Klawonn, F., Höppner, F., 2003b. What is fuzzy about fuzzy clustering? Understanding and improving the concept of the fuzzifier. In: Advances in Intelligent Data Analysis V, Lecture Notes in Computer Science, vol. 2810. Springer GmbH, Berlin, pp. 254-264, 3-540-40383-3.
  • 34
    • 0030214781 scopus 로고    scopus 로고
    • The possibilistic c-means algorithm: insights and recommendations
    • Krishnapuram R., and Keller J. The possibilistic c-means algorithm: insights and recommendations. IEEE Trans. Fuzzy Systems 4 (1996) 385-393
    • (1996) IEEE Trans. Fuzzy Systems , vol.4 , pp. 385-393
    • Krishnapuram, R.1    Keller, J.2
  • 35
    • 0018468345 scopus 로고
    • A comparison of three methods for selecting values of input variables in the analysis of output from a computer code
    • McKay M.D., Beckman R.J., and Conover W.J. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21 2 (1979) 239-245
    • (1979) Technometrics , vol.21 , Issue.2 , pp. 239-245
    • McKay, M.D.1    Beckman, R.J.2    Conover, W.J.3
  • 38
    • 33750366084 scopus 로고    scopus 로고
    • Runkler, T.A., Bezdek, J.C., 1998. Race: relational alternating cluster estimation and the wedding table problem. In: Brauer, W. (Ed.), Fuzzy-Neuro-Systems '98, München, Proceedings in Artificial Intelligence, vol. 7, pp. 330-337
  • 40
    • 0014534297 scopus 로고
    • A new approach to clustering
    • Ruspini E.H. A new approach to clustering. Inform. Control 15 1 (1969) 22-32
    • (1969) Inform. Control , vol.15 , Issue.1 , pp. 22-32
    • Ruspini, E.H.1
  • 42
    • 0036779072 scopus 로고    scopus 로고
    • Alternating c-means clustering algorithms
    • Wu K., and Yang M. Alternating c-means clustering algorithms. Pattern Recognition 35 (2002) 2267-2278
    • (2002) Pattern Recognition , vol.35 , pp. 2267-2278
    • Wu, K.1    Yang, M.2
  • 43


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.