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Volumn , Issue , 2014, Pages 432-441

The setwise stream classification problem

Author keywords

data classification; data streams

Indexed keywords


EID: 84907022384     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2623330.2623751     Document Type: Conference Paper
Times cited : (9)

References (25)
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  • 4
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    • Aggarwal, C.1
  • 5
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    • Aggarwal, C.1
  • 10
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    • Solving the Multiple-Instance Problem with Axis-Parallel Rectangles
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    • (1997) Artificial Intelligence , vol.89 , pp. 31-71
    • Dietterich, T.1    Lathrop, R.2    Lozano-Perez, T.3
  • 13
    • 12244286335 scopus 로고    scopus 로고
    • Systematic Data Selection to Mine Concept Drifting Data Streams
    • W. Fan. Systematic Data Selection to Mine Concept Drifting Data Streams, ACM KDD Conference, pp. 128-137, 2004.
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  • 14
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    • Accurate Decision Trees for Mining High-Speed Data Streams
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    • Gama, J.1    Rocha, R.2    Medas, P.3
  • 16
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    • Efficient Decision Tree Construction on Streaming Data
    • R. Jin, and G. Agrawal. Efficient Decision Tree Construction on Streaming Data, ACM KDD Conference, pp. 571-576, 2003.
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    • Jin, R.1    Agrawal, G.2
  • 20
    • 72749128301 scopus 로고    scopus 로고
    • The Set Classification Problem and Solution Methods
    • X. Ning, and G. Karypis. The Set Classification Problem and Solution Methods. SIAM Conference on Data Mining, pp. 847-858, 2009.
    • (2009) SIAM Conference on Data Mining , pp. 847-858
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  • 22
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    • Mining Concept-Drifting Data Streams using Ensemble Classifiers
    • H. Wang, W. Fan, P. Yu, J. Han. Mining Concept-Drifting Data Streams using Ensemble Classifiers. ACM KDD Conference, pp. 226-235, 2003.
    • (2003) ACM KDD Conference , pp. 226-235
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  • 25
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    • Fast Density Estimation Using CF-Kernel for Very Large Databases
    • T. Zhang, R. Ramakrishnan, M. Livny. Fast Density Estimation Using CF-Kernel for Very Large Databases. ACM KDD Conference, pp. 312-316, 1999.
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    • Zhang, T.1    Ramakrishnan, R.2    Livny, M.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.