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Volumn 2, Issue , 2005, Pages 1065-1069

A hierarchical naive bayes mixture model for name disambiguation in author citations

Author keywords

Feature Selection; Name Disambiguation; Unsupervised Learning

Indexed keywords

FEATURE SELECTION; NAME DISAMBIGUATION; UNSUPERVISED LEARNING;

EID: 33644522028     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1066677.1066920     Document Type: Conference Paper
Times cited : (76)

References (20)
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    • Gillman, P.1
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    • Real-world data is dirty: Data cleansing and the merge/purge problem
    • M. A. Hernandez and S. J. Stolfo. Real-world data is dirty: Data cleansing and the merge/purge problem. Data Mining and Knowledge Discovery, 2(1):9-37, 1998.
    • (1998) Data Mining and Knowledge Discovery , vol.2 , Issue.1 , pp. 9-37
    • Hernandez, M.A.1    Stolfo, S.J.2
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    • Efficient clustering of high-dimensional data sets with application to reference matching
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    • (2000) Knowledge Discovery and Data Mining , pp. 169-178
    • McCallum, A.1    Nigam, K.2    Ungar, L.H.3
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    • An efficient domain-independent algorithm for detecting approximately duplicate database records
    • A. E. Monge and C. Elkan. An efficient domain-independent algorithm for detecting approximately duplicate database records. In Research Issues on Data Mining and Knowledge Discovery, pages 23-29, 1997.
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    • Monge, A.E.1    Elkan, C.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.