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Volumn 239, Issue , 2013, Pages 154-164

Examination and comparison of conflicting data in granulated datasets: Equal width interval vs. equal frequency interval

Author keywords

Conflicting data; Data mining; Granulation

Indexed keywords

CONFLICTING DATA; DATA SET SIZE; FREQUENCY INTERVALS; GRANULATION TECHNIQUES; KNOWLEDGE DISCOVERY FROM DATABASE; NUMBER OF CLASS; PRODUCTION OF;

EID: 84876911778     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2013.03.014     Document Type: Article
Times cited : (16)

References (35)
  • 4
    • 0030285403 scopus 로고    scopus 로고
    • The KDD process for extracting useful knowledge form volumes of data
    • U.M. Fayyad, G. Piatetsky-Shapiro, and P. Smyth The KDD process for extracting useful knowledge form volumes of data Communication ACM 39 1996 1996 27 41
    • (1996) Communication ACM , vol.39 , Issue.1996 , pp. 27-41
    • Fayyad, U.M.1    Piatetsky-Shapiro, G.2    Smyth, P.3
  • 5
    • 0030284618 scopus 로고    scopus 로고
    • A database perspective on knowledge discovery
    • T. Imielinski, and H. Mannila A database perspective on knowledge discovery Communication of ACM 39 1996 58 64
    • (1996) Communication of ACM , vol.39 , pp. 58-64
    • Imielinski, T.1    Mannila, H.2
  • 7
    • 22144499172 scopus 로고    scopus 로고
    • On the granulation simplicity for the decision rule discovery in databases: EWI Vs
    • C.H. Wu On the granulation simplicity for the decision rule discovery in databases: EWI Vs EFI, International Journal of Science and Technology 14 2003 28 36
    • (2003) EFI, International Journal of Science and Technology , vol.14 , pp. 28-36
    • Wu, C.H.1
  • 8
    • 33845390782 scopus 로고    scopus 로고
    • Granular computing: An overview
    • A. Abraham, B. De Baets, M. Koppen, B. Nickolay, Springer London
    • W. Pedrycz Granular computing: an overview A. Abraham, B. De Baets, M. Koppen, B. Nickolay, Advance Soft Computing, vol. 34 2006 Springer London 19 34
    • (2006) Advance Soft Computing, Vol. 34 , pp. 19-34
    • Pedrycz, W.1
  • 10
    • 84886560704 scopus 로고    scopus 로고
    • Fundamentals of interval analysis and linkages to fuzzy set theory
    • W. Pedeycz, A. Skowron, V. Kreinovich, John Wiley & Sons Ltd. Chichester, West Sussex Chapter 3
    • W.A. Lodick Fundamentals of interval analysis and linkages to fuzzy set theory W. Pedeycz, A. Skowron, V. Kreinovich, Handbook of Granular Computing 2008 John Wiley & Sons Ltd. Chichester, West Sussex Chapter 3
    • (2008) Handbook of Granular Computing
    • Lodick, W.A.1
  • 12
    • 1642469977 scopus 로고    scopus 로고
    • Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic
    • L.A. Zadeh Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic Fuzzy Sets and Systems 90 1997 111 117
    • (1997) Fuzzy Sets and Systems , vol.90 , pp. 111-117
    • Zadeh, L.A.1
  • 13
    • 70449102674 scopus 로고    scopus 로고
    • A new extension of fuzzy sets using rough sets: R-fuzzy sets
    • Y. Yang, and C. Hinde A new extension of fuzzy sets using rough sets: R-fuzzy sets Information Sciences 180 2010 354 365
    • (2010) Information Sciences , vol.180 , pp. 354-365
    • Yang, Y.1    Hinde, C.2
  • 14
    • 0035493279 scopus 로고    scopus 로고
    • On efficient handling of continuous attributes in large database
    • H.S. Nguyen On efficient handling of continuous attributes in large database Fundamenda Informaticae 48 2001 61 81
    • (2001) Fundamenda Informaticae , vol.48 , pp. 61-81
    • Nguyen, H.S.1
  • 17
    • 0034274083 scopus 로고    scopus 로고
    • Data mining and machine oriented modeling: A granular computing approach
    • T.Y. Lin Data mining and machine oriented modeling: a granular computing approach Journal of Applied Intelligence 13 2000 113 124
    • (2000) Journal of Applied Intelligence , vol.13 , pp. 113-124
    • Lin, T.Y.1
  • 19
    • 85139983802 scopus 로고
    • Supervised and unsupervised discretization of continuous features
    • A. Prieditis, S. Russell (Eds.) Morgan Kaufmann, CA
    • J. Dougherty, R. Kohavi, M. Sahami, Supervised and unsupervised discretization of continuous features, in: A. Prieditis, S. Russell (Eds.), Proc. of 1995 International Conference on Machine Learning, Morgan Kaufmann, CA, 1995, pp. 194-202.
    • (1995) Proc. of 1995 International Conference on Machine Learning , pp. 194-202
    • Dougherty, J.1    Kohavi, R.2    Sahami, M.3
  • 23
    • 35748943218 scopus 로고    scopus 로고
    • A discretization algorithm based on class-attribute contingency coefficient
    • C.-J. Tsai, C.-L. Lee, and W.-P. Yang A discretization algorithm based on class-attribute contingency coefficient Information Sciences 178 2008 714 731
    • (2008) Information Sciences , vol.178 , pp. 714-731
    • Tsai, C.-J.1    Lee, C.-L.2    Yang, W.-P.3
  • 25
    • 0036832986 scopus 로고    scopus 로고
    • TCRM: Diagnosing tuple inconsistency for granulated datasets
    • C.H. Wu TCRM: Diagnosing tuple inconsistency for granulated datasets Knowledge-Based Systems 15 2002 507 514
    • (2002) Knowledge-Based Systems , vol.15 , pp. 507-514
    • Wu, C.H.1
  • 26
    • 37749034335 scopus 로고
    • On changing continuous attributes into ordered discrete attributes
    • Springer-Verlag Berlin
    • J. Catlett On changing continuous attributes into ordered discrete attributes Proc. of the European Working Session on Learning 1991 Springer-Verlag Berlin 164 178
    • (1991) Proc. of the European Working Session on Learning , pp. 164-178
    • Catlett, J.1
  • 28
    • 33749007698 scopus 로고    scopus 로고
    • MODL: A Bayes optimal discretization method for continuous attributes
    • M. Boule MODL: a Bayes optimal discretization method for continuous attributes Machine Learning 65 2006 131 165
    • (2006) Machine Learning , vol.65 , pp. 131-165
    • Boule, M.1
  • 34
    • 84876928878 scopus 로고    scopus 로고
    • http://www.ics.uci.edu/~mlearn/.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.