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Volumn 13, Issue 2-3, 1997, Pages 231-242

Systems for KDD: From concepts to practice

Author keywords

Data mining; Database systems; Knowledge discovery; Systems integration

Indexed keywords

DATA ACQUISITION; KNOWLEDGE ACQUISITION; KNOWLEDGE BASED SYSTEMS;

EID: 0031270378     PISSN: 0167739X     EISSN: None     Source Type: Journal    
DOI: 10.1016/s0167-739x(97)00023-x     Document Type: Article
Times cited : (12)

References (23)
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  • 4
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    • a revised version to appear
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  • 6
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    • Overcoming the Myopia of inductive learning algorithms with ReliefF
    • I. Kononenko, E. Šimec and M. Robnik, Overcoming the Myopia of inductive learning algorithms with ReliefF, Appl. Intelligence 7 (1997) 39-55.
    • (1997) Appl. Intelligence , vol.7 , pp. 39-55
    • Kononenko, I.1    Šimec, E.2    Robnik, M.3
  • 10
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    • Bias in information-based measures in decision tree induction
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    • White, A.P.1    Liu, W.Z.2
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    • An information theoretic approach to rule induction from databases
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    • Smyth, P.1    Goodman, R.M.2
  • 21
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    • C. Apte, S.J. Hong, J.R.M. Hosking, J. Lepre, E. Pednault and B. Rosen, Decomposition of heterogeneous classification problems, IBM Research Report, RC-20514, March 1996; in: Proc. 2nd Internat. Conf. on Intelligent Data Analysis, IDA-97 (1997).
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.