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Volumn 1081, Issue , 1996, Pages 429-441

Attribute selection strategies for attribute-oriented generalization

Author keywords

Applications; Knowledge acquisition; Knowledge representation; Learning

Indexed keywords


EID: 84957883726     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-61291-2_70     Document Type: Conference Paper
Times cited : (2)

References (16)
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  • 2
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  • 3
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    • Performance Evaluation of Attribute-Oriented Algorithms for Knowledge Discovery from Databases
    • Nov. 5-8, Herndon, Virginia, USA
    • C. L. Carter and H. J. Hamilton, “Performance Evaluation of Attribute-Oriented Algorithms for Knowledge Discovery from Databases”, Seventh International Conference on Tools with Artificial Intelligence (TAI 95), Nov. 5-8, 1995, Herndon, Virginia, USA, 486-489.
    • (1995) Seventh International Conference on Tools with Artificial Intelligence (TAI 95) , pp. 486-489
    • Carter, C.L.1    Hamilton, H.J.2
  • 4
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    • The Software Architecture of DBLEARN, “Technical Report CS-94-04”, University of Regina
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    • (1994)
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  • 6
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    • Knowledge Discovery in Databases: An Overview
    • G. Piatetsky-Shapiro and W. J. Frawley, Eds., AAAI/MIT Press, Menlo Park, CA
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    • Frawley, W.J.1    Piatetsky-Shapiro, G.2    Matheus, C.J.3
  • 7
    • 0029308414 scopus 로고
    • Estimating DBLEARN’s Potential for Knowledge Discovery in Databases
    • H. J. Hamilton and D. F. Fudger, “Estimating DBLEARN’s Potential for Knowledge Discovery in Databases”, Computational Intelligence, 11(2), 1995, 1-18.
    • (1995) Computational Intelligence , vol.11 , Issue.2 , pp. 1-18
    • Hamilton, H.J.1    Fudger, D.F.2
  • 10
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    • Knowledge Discovery in Databases: Papers from the 1993 Workshop, Technical Report WS-93-02, AAAI Press, Menlo Park, CA
    • J. A. Major and J. J. Mangano, “Selecting Among Rules Induced from a Hurricane Database”, Knowledge Discovery in Databases: Papers from the 1993 Workshop, Technical Report WS-93-02, AAAI Press, Menlo Park, CA., 1-13.
    • Selecting among Rules Induced from a Hurricane Database , pp. 1-13
    • Major, J.A.1    Mangano, J.J.2
  • 11
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    • Selecting and Reporting What is Interesting: The KEFIR Application to Healthcare Data
    • U. Fayyad, G. Piatetsky-Shapiro, P. Smyth and R. Uthurusamy, Eds., AAAI/MIT Press, Menlo Park, CA
    • C. J. Matheus, G. Piatetsky-Shapiro and D. McNeill, “Selecting and Reporting What is Interesting: The KEFIR Application to Healthcare Data”, in: U. Fayyad, G. Piatetsky-Shapiro, P. Smyth and R. Uthurusamy, Eds., Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press, Menlo Park, CA., 1995, 401-419.
    • (1995) Advances in Knowledge Discovery and Data Mining , pp. 401-419
    • Matheus, C.J.1    Piatetsky-Shapiro, G.2    McNeill, D.3
  • 12
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    • Discovery, Analysis and Presentation of Strong Rules
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    • G. Piatetsky-Shapiro, “Discovery, Analysis and Presentation of Strong Rules”, in: G. Piatetsky-Shapiro and W. J. Frawley, Eds., Knowledge Discovery in Databases, AAAI/MIT Press, Menlo Park, CA., 1991, 229-248.
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  • 13
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  • 14
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    • GRG: Knowledge Discovery Using Information Generalization, Information Reduction and Rule Generation
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    • N. Shan, H. J. Hamilton and N. Cercone, “GRG: Knowledge Discovery Using Information Generalization, Information Reduction and Rule Generation”, 7th IEEE International Conference on Tools with Artificial Intelligence, Washington, D.C., November, 1995. 372-379.
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    • Rule Induction using Information Theory
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    • P. Smyth and R. M. Goodman, “Rule Induction using Information Theory”, in: Piatetsky-Shapiro G. and Frawley W. J., Eds., Knowledge Discovery in Databases, AAAI/MIT Press, Menlo Park, CA., 1991,159-176.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.