메뉴 건너뛰기




Volumn 30, Issue 11, 2009, Pages 1015-1026

RECM: Relational evidential c-means algorithm

Author keywords

Belief functions; Clustering; Dempster Shafer theory; Proximity data; Unsupervised learning

Indexed keywords

BELIEF FUNCTIONS; C-MEANS; C-MEANS ALGORITHMS; CLUSTERING; DATA SETS; DEMPSTER-SHAFER THEORY; GRADIENT BASED; OPTIMIZATION PROCEDURES; POSSIBILISTIC; PROXIMITY DATA; STRUCTURE-BASED; THEORY OF BELIEF FUNCTIONS; VECTORIAL DATA;

EID: 67649090202     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2009.04.008     Document Type: Article
Times cited : (65)

References (32)
  • 5
    • 0000586827 scopus 로고
    • Characterization and detection of noise in clustering
    • Davé R.N. 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.N.1
  • 6
    • 0031644831 scopus 로고    scopus 로고
    • Clustering of relational data containing noise and outliers
    • Davé, R.N., 1998. Clustering of relational data containing noise and outliers. In: FUZZ'IEEE 98, vol. 2, pp. 1411-1416.
    • (1998) FUZZ'IEEE 98 , vol.2 , pp. 1411-1416
    • Davé, R.N.1
  • 7
    • 0029307876 scopus 로고
    • A k-nearest neighbor classification rule based on Dempster-Shafer theory
    • Denœux T. A k-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE Trans. Syst. Man Cybernet. 25 05 (1995) 804-813
    • (1995) IEEE Trans. Syst. Man Cybernet. , vol.25 , Issue.5 , pp. 804-813
    • Denœux, T.1
  • 9
  • 10
    • 33947108026 scopus 로고    scopus 로고
    • Classification using belief functions: The relationship between the case-based and model-based approaches
    • Denœux T., and Smets P. Classification using belief functions: The relationship between the case-based and model-based approaches. IEEE Trans. Syst. Man Cybernet. B 36 6 (2006) 1395-1406
    • (2006) IEEE Trans. Syst. Man Cybernet. B , vol.36 , Issue.6 , pp. 1395-1406
    • Denœux, T.1    Smets, P.2
  • 11
    • 21244468777 scopus 로고    scopus 로고
    • Combining multiple clusterings using evidence accumulation
    • Fred A.L.N., and Jain A.K. Combining multiple clusterings using evidence accumulation. IEEE Trans. Pattern Anal. Machine Intell. 27 6 (2005) 835-850
    • (2005) IEEE Trans. Pattern Anal. Machine Intell. , vol.27 , Issue.6 , pp. 835-850
    • Fred, A.L.N.1    Jain, A.K.2
  • 12
    • 0036565280 scopus 로고    scopus 로고
    • Mercer kernel-based clustering in feature space
    • Girolami M. Mercer kernel-based clustering in feature space. IEEE Trans. Neural Netw. 13 3 (2002) 780-784
    • (2002) IEEE Trans. Neural Netw. , vol.13 , Issue.3 , pp. 780-784
    • Girolami, M.1
  • 14
    • 0028667335 scopus 로고
    • Nerf c-means: Non-Euclidean relational fuzzy clustering
    • Hathaway R.J., and Bezdek J.C. Nerf c-means: Non-Euclidean relational fuzzy clustering. Pattern Recognition 27 (1994) 429-437
    • (1994) Pattern Recognition , vol.27 , pp. 429-437
    • Hathaway, R.J.1    Bezdek, J.C.2
  • 17
    • 0027595430 scopus 로고
    • A possibilistic approach to clustering
    • Krishnapuram R., and Keller J. A possibilistic approach to clustering. IEEE Trans. Fuzzy Syst. 1 2 (1993) 98-110
    • (1993) IEEE Trans. Fuzzy Syst. , vol.1 , Issue.2 , pp. 98-110
    • Krishnapuram, R.1    Keller, J.2
  • 18
    • 0035416012 scopus 로고    scopus 로고
    • Low-complexity fuzzy relational clustering algorithms for web mining
    • Krishnapuram R., Joshi A., Nasraoui O., and Yi L. Low-complexity fuzzy relational clustering algorithms for web mining. IEEE Trans. Fuzzy Syst. 9 (2001) 595-608
    • (2001) IEEE Trans. Fuzzy Syst. , vol.9 , pp. 595-608
    • Krishnapuram, R.1    Joshi, A.2    Nasraoui, O.3    Yi, L.4
  • 19
    • 33745428531 scopus 로고    scopus 로고
    • Feature discovery in non-metric pairwise data
    • Laub J., and Muller K.-R. Feature discovery in non-metric pairwise data. J. Machine Learn. Res. 5 (2004) 801-818
    • (2004) J. Machine Learn. Res. , vol.5 , pp. 801-818
    • Laub, J.1    Muller, K.-R.2
  • 20
    • 0346216895 scopus 로고    scopus 로고
    • Clustering interval-valued data using belief functions
    • Masson M.-H., and Denœux T. Clustering interval-valued data using belief functions. Pattern Recognition Lett. 25 2 (2004) 163-171
    • (2004) Pattern Recognition Lett. , vol.25 , Issue.2 , pp. 163-171
    • Masson, M.-H.1    Denœux, T.2
  • 21
    • 36749023291 scopus 로고    scopus 로고
    • ECM: An evidential version of the fuzzy c-means algorithm
    • Masson M.-H., and Denœux T. ECM: An evidential version of the fuzzy c-means algorithm. Pattern Recognition 41 (2008) 1384-1397
    • (2008) Pattern Recognition , vol.41 , pp. 1384-1397
    • Masson, M.-H.1    Denœux, T.2
  • 22
    • 33846058836 scopus 로고    scopus 로고
    • Pairwise classifier combination using belief functions
    • Quost B., Denoeux T., and Masson M.-H. Pairwise classifier combination using belief functions. Pattern Recognition Lett. 28 5 (2007) 644-653
    • (2007) Pattern Recognition Lett. , vol.28 , Issue.5 , pp. 644-653
    • Quost, B.1    Denoeux, T.2    Masson, M.-H.3
  • 23
    • 0001167440 scopus 로고
    • Pattern classification problems and fuzzy sets
    • Roubens M. Pattern classification problems and fuzzy sets. Fuzzy Sets Syst. 1 (1978) 239-253
    • (1978) Fuzzy Sets Syst. , vol.1 , pp. 239-253
    • Roubens, M.1
  • 24
    • 0037309521 scopus 로고    scopus 로고
    • Web mining with relational clustering
    • Runkler T.A., and Bezdek J.C. Web mining with relational clustering. Int. J. Approx. Reason. 32 2-3 (2003) 217-236
    • (2003) Int. J. Approx. Reason. , vol.32 , Issue.2-3 , pp. 217-236
    • Runkler, T.A.1    Bezdek, J.C.2
  • 25
    • 84887006810 scopus 로고
    • A non-linear mapping for data structure analysis
    • Sammon J.W. A non-linear mapping for data structure analysis. IEEE Trans. Comput. 18 (1969) 401-409
    • (1969) IEEE Trans. Comput. , vol.18 , pp. 401-409
    • Sammon, J.W.1
  • 27
    • 0025434991 scopus 로고
    • The combination of evidence in the Transferable Belief Model
    • Smets P. The combination of evidence in the Transferable Belief Model. IEEE Trans. Pattern Anal. Machine Intell. 12 5 (1990) 447-458
    • (1990) IEEE Trans. Pattern Anal. Machine Intell. , vol.12 , Issue.5 , pp. 447-458
    • Smets, P.1
  • 28
    • 9644266682 scopus 로고    scopus 로고
    • Decision making in the TBM: The necessity of the pignistic transformation
    • Smets P. Decision making in the TBM: The necessity of the pignistic transformation. Int. J. Approx. Reason. 38 5 (2005) 133-147
    • (2005) Int. J. Approx. Reason. , vol.38 , Issue.5 , pp. 133-147
    • Smets, P.1
  • 29
    • 0028406490 scopus 로고
    • The transferable belief model
    • Smets P., and Kennes R. The transferable belief model. Artificial Intell. 66 (1994) 191-243
    • (1994) Artificial Intell. , vol.66 , pp. 191-243
    • Smets, P.1    Kennes, R.2
  • 30
    • 60049085522 scopus 로고    scopus 로고
    • Fuzzy c-means clustering with prior biological knowledge
    • Tari L., Baral C., and Kim S. Fuzzy c-means clustering with prior biological knowledge. J. Biomed. Inform. 42 1 (2009) 74-81
    • (2009) J. Biomed. Inform. , vol.42 , Issue.1 , pp. 74-81
    • Tari, L.1    Baral, C.2    Kim, S.3
  • 31
    • 0000881417 scopus 로고
    • Numerical classification of proximity data with assignment measures
    • Windham M.P. Numerical classification of proximity data with assignment measures. J. classificat. 2 (1985) 157-172
    • (1985) J. classificat. , vol.2 , pp. 157-172
    • Windham, M.P.1
  • 32
    • 0002361037 scopus 로고
    • Discussion of a set of points in terms of their mutual distances
    • Young G., and Householder A.S. Discussion of a set of points in terms of their mutual distances. Psychometrika 3 (1938) 19-22
    • (1938) Psychometrika , vol.3 , pp. 19-22
    • Young, G.1    Householder, A.S.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.