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Volumn 55, Issue 1 PART 2, 2014, Pages 238-258

Qualitative and quantitative combinations of crisp and rough clustering schemes using dominance relations

Author keywords

Combination of clustering schemes; Crisp clustering; Dominance relations; Preference relations; Qualitative reasoning; Rough clustering

Indexed keywords

CLUSTERING ALGORITHMS;

EID: 84888074678     PISSN: 0888613X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijar.2013.05.007     Document Type: Article
Times cited : (30)

References (60)
  • 4
    • 85006313442 scopus 로고    scopus 로고
    • An outline of a theory of three-way decisions
    • J. Yao, Y. Yang, R. Slowinski, S. Greco, H. Li, S. Mitra, L. Polkowski (Eds.)
    • Y.Y. Yao, An outline of a theory of three-way decisions, in: J. Yao, Y. Yang, R. Slowinski, S. Greco, H. Li, S. Mitra, L. Polkowski (Eds.), Proc. of the 2012 Rough Sets and Current Trends in Computing, 2012, pp. 1-17.
    • (2012) Proc. of the 2012 Rough Sets and Current Trends in Computing , pp. 1-17
    • Yao, Y.Y.1
  • 7
    • 0002608174 scopus 로고
    • A decision theoretic framework for approximating concepts
    • Y.Y. Yao, and S. Wong A decision theoretic framework for approximating concepts International Journal of Man-Machine Studies 37 6 1992 793 809
    • (1992) International Journal of Man-Machine Studies , vol.37 , Issue.6 , pp. 793-809
    • Yao, Y.Y.1    Wong, S.2
  • 10
    • 84860260862 scopus 로고    scopus 로고
    • A multiple-category classification approach with decision-theoretic rough sets
    • D. Liu, T. Li, and H. Li A multiple-category classification approach with decision-theoretic rough sets Fundamenta Informaticae 115 2012 173 188
    • (2012) Fundamenta Informaticae , vol.115 , pp. 173-188
    • Liu, D.1    Li, T.2    Li, H.3
  • 11
    • 84860166334 scopus 로고    scopus 로고
    • Modelling multi-agent three-way decisions with decision-theoretic rough sets
    • X. Yang, and J.T. Yao Modelling multi-agent three-way decisions with decision-theoretic rough sets Fundamenta Informaticae 115 2012 157 171
    • (2012) Fundamenta Informaticae , vol.115 , pp. 157-171
    • Yang, X.1    Yao, J.T.2
  • 12
    • 45849092954 scopus 로고    scopus 로고
    • Attribute reduction in decision-theoretic rough set models
    • Y.Y. Yao, and Y. Zhao Attribute reduction in decision-theoretic rough set models Information Sciences 178 17 2008 3356 3373
    • (2008) Information Sciences , vol.178 , Issue.17 , pp. 3356-3373
    • Yao, Y.Y.1    Zhao, Y.2
  • 16
    • 0037117684 scopus 로고    scopus 로고
    • Rough sets methodology for sorting problems in presence of multiple attributes and criteria
    • S. Greco, B. Matarazzo, and R. Slowinski Rough sets methodology for sorting problems in presence of multiple attributes and criteria European Journal of Operation Research 138 2 2002 247 259
    • (2002) European Journal of Operation Research , vol.138 , Issue.2 , pp. 247-259
    • Greco, S.1    Matarazzo, B.2    Slowinski, R.3
  • 17
  • 18
    • 0015644825 scopus 로고
    • A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters
    • 10.1080/01969727308546046
    • J.C. Dunn A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters Cybernetics 3 3 1973 32 57 10.1080/01969727308546046
    • (1973) Cybernetics , vol.3 , Issue.3 , pp. 32-57
    • Dunn, J.C.1
  • 19
    • 84941155240 scopus 로고
    • Well separated clusters and optimal fuzzy partitions
    • J.C. Dunn Well separated clusters and optimal fuzzy partitions Cybernetics 4 1974 95 104
    • (1974) Cybernetics , vol.4 , pp. 95-104
    • Dunn, J.C.1
  • 25
    • 36749023291 scopus 로고    scopus 로고
    • ECM: An evidential version of the fuzzy c-means algorithm
    • M.H. Masson, and T. Denoeux 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    Denoeux, T.2
  • 31
    • 0036471411 scopus 로고    scopus 로고
    • Nonhierarchical document clustering by a tolerance rough set model
    • T.B. Ho, and N.B. Nguyen Nonhierarchical document clustering by a tolerance rough set model International Journal of Intelligent Systems 17 2 2002 199 212
    • (2002) International Journal of Intelligent Systems , vol.17 , Issue.2 , pp. 199-212
    • Ho, T.B.1    Nguyen, N.B.2
  • 32
    • 4544352979 scopus 로고    scopus 로고
    • An evolutionary rough partitive clustering
    • S. Mitra An evolutionary rough partitive clustering Pattern Recognition Letters 25 12 2004 1439 1449
    • (2004) Pattern Recognition Letters , vol.25 , Issue.12 , pp. 1439-1449
    • Mitra, S.1
  • 34
    • 84889387072 scopus 로고    scopus 로고
    • Rough document clustering and the internet
    • John Wiley & Sons Hoboken, NJ
    • H.S. Nguyen, and T.B. Ho Rough document clustering and the internet Handbook on Granular Computing 2007 John Wiley & Sons Hoboken, NJ 987 1005
    • (2007) Handbook on Granular Computing , pp. 987-1005
    • Nguyen, H.S.1    Ho, T.B.2
  • 35
    • 33646419553 scopus 로고    scopus 로고
    • Some refinements of rough k-means
    • G. Peters Some refinements of rough k-means Pattern Recognition 39 8 2006 1481 1491
    • (2006) Pattern Recognition , vol.39 , Issue.8 , pp. 1481-1491
    • Peters, G.1
  • 37
    • 0035416470 scopus 로고    scopus 로고
    • Unsupervised rough set classification using gas
    • P. Lingras Unsupervised rough set classification using gas Journal of Intelligent Information Systems 16 3 2001 215 228
    • (2001) Journal of Intelligent Information Systems , vol.16 , Issue.3 , pp. 215-228
    • Lingras, P.1
  • 38
  • 39
    • 11144312947 scopus 로고    scopus 로고
    • Interval set clustering of web users using modified Kohonen self-organizing maps based on the properties of rough sets
    • P. Lingras, M. Hogo, and M. Snorek Interval set clustering of web users using modified Kohonen self-organizing maps based on the properties of rough sets Web Intelligence and Agent Systems 2 3 2004 217 230
    • (2004) Web Intelligence and Agent Systems , vol.2 , Issue.3 , pp. 217-230
    • Lingras, P.1    Hogo, M.2    Snorek, M.3
  • 40
    • 57049184601 scopus 로고    scopus 로고
    • Precision of rough set clustering
    • C.-C. Chan, J. Grzymala-Busse, W. Ziarko, Lecture Notes in Computer Science Springer Berlin/Heidelberg 10.1007/978-3-540-88425-5-38
    • P. Lingras, M. Chen, and D. Miao Precision of rough set clustering C.-C. Chan, J. Grzymala-Busse, W. Ziarko, Rough Sets and Current Trends in Computing Lecture Notes in Computer Science vol. 5306 2008 Springer Berlin/Heidelberg 369 378 10.1007/978-3-540-88425-5-38
    • (2008) Rough Sets and Current Trends in Computing , vol.5306 , pp. 369-378
    • Lingras, P.1    Chen, M.2    Miao, D.3
  • 43
    • 84860162832 scopus 로고    scopus 로고
    • Autonomous knowledge-oriented clustering using decision-theoretic rough set theory
    • H. Yu, and S. Chu Autonomous knowledge-oriented clustering using decision-theoretic rough set theory Fundamenta Informaticae 115 2012 141 156
    • (2012) Fundamenta Informaticae , vol.115 , pp. 141-156
    • Yu, H.1    Chu, S.2
  • 45
    • 0001138328 scopus 로고
    • Algorithm as136: A k-means clustering algorithm
    • J.A. Hartigan, and M.A. Wong Algorithm as136: a k-means clustering algorithm Applied Statistics 28 1979 100 108
    • (1979) Applied Statistics , vol.28 , pp. 100-108
    • Hartigan, J.A.1    Wong, M.A.2
  • 48
    • 84888019221 scopus 로고    scopus 로고
    • On an optimization representation of decision-theoretic rough set model
    • doi:10.1016/j.ijar.2013.02.010
    • X. Jia, Z. Tang, W. Liao, L. Shang, On an optimization representation of decision-theoretic rough set model, International Journal of Approximate Reasoning. http://dx.doi.org/10.1016/j.ijar.2013.02.010.
    • International Journal of Approximate Reasoning
    • Jia, X.1    Tang, Z.2    Liao, W.3    Shang, L.4
  • 50
    • 84888028049 scopus 로고    scopus 로고
    • An axiomatic characterization of probabilistic rough sets
    • doi:10.1016/j.ijar.2013.02.012
    • T.-J. Li, X.-P. Yang, An axiomatic characterization of probabilistic rough sets, International Journal of Approximate Reasoning. http://dx.doi.org/ 10.1016/j.ijar.2013.02.012.
    • International Journal of Approximate Reasoning
    • Li, T.-J.1    Yang, X.-P.2
  • 51
    • 84888023478 scopus 로고    scopus 로고
    • Incorporating logistic regression to decision-theoretic rough sets for classifications
    • doi:10.1016/j.ijar.2013.02.013
    • D. Liu, T. Li, D. Liang, Incorporating logistic regression to decision-theoretic rough sets for classifications, International Journal of Approximate Reasoning. http://dx.doi.org/10.1016/j.ijar.2013.02.013.
    • International Journal of Approximate Reasoning
    • Liu, D.1    Li, T.2    Liang, D.3
  • 53
    • 84888040083 scopus 로고    scopus 로고
    • Analyzing uncertainties of probabilistic rough set regions with game-theoretic rough sets
    • doi:10.1016/j.ijar.2013.03.015
    • N. Azam, J.T. Yao, Analyzing uncertainties of probabilistic rough set regions with game-theoretic rough sets, International Journal of Approximate Reasoning. http://dx.doi.org/10.1016/j.ijar.2013.03.015.
    • International Journal of Approximate Reasoning
    • Azam, N.1    Yao, J.T.2
  • 56
    • 84888016689 scopus 로고    scopus 로고
    • Multi-class decision-theoretic rough sets
    • doi:10.1016/j.ijar.2013.04.006
    • B. Zhou, Multi-class decision-theoretic rough sets, International Journal of Approximate Reasoning. http://dx.doi.org/10.1016/j.ijar.2013.04.006.
    • International Journal of Approximate Reasoning
    • Zhou, B.1
  • 58
    • 80054087122 scopus 로고    scopus 로고
    • H. Yu, Z. Liu, G. Wang, Automatically determining the number of clusters using decision-theoretic rough set, in: Proc. of the 2011 Rough Sets and Knowledge Technology, 2011, pp. 504-513.
    • H. Yu, Z. Liu, G. Wang, Automatically determining the number of clusters using decision-theoretic rough set, in: Proc. of the 2011 Rough Sets and Knowledge Technology, 2011, pp. 504-513.
  • 59
    • 84888044158 scopus 로고    scopus 로고
    • doi:10.1016/j.ijar.2013.03.018 H. Yu, Z. Liu, G. Wang, An automatic method to determine the number of clusters using decision-theoretic rough set, International Journal of Approximate Reasoning.
    • H. Yu, Z. Liu, G. Wang, An automatic method to determine the number of clusters using decision-theoretic rough set, International Journal of Approximate Reasoning. http://dx.doi.org/10.1016/j.ijar.2013.03.018.
  • 60
    • 0001168081 scopus 로고    scopus 로고
    • Applications of rough patterns
    • L. Polkowski, A. Skowron, Physica Verlag (Springer) Berlin (Chapter 3)
    • P. Lingras Applications of rough patterns L. Polkowski, A. Skowron, Rough Sets in Data Mining and Knowledge Discovery, Soft Computing 1998 Physica Verlag (Springer) Berlin 369 384 (Chapter 3)
    • (1998) Rough Sets in Data Mining and Knowledge Discovery, Soft Computing , pp. 369-384
    • Lingras, P.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.