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Volumn , Issue , 2001, Pages 377-384

Analyzing the interestingness of association rules from the temporal dimension

Author keywords

[No Author keywords available]

Indexed keywords


EID: 78149338936     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (49)

References (28)
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    • Mining the most interesting rules
    • Bayardo, R. and Agrawal, R. "Mining the most interesting rules" KDD-99, 1999.
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    • Dong, G.1    Li, J.2
  • 6
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    • A framework for measuring changes in data characteristics
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    • POPS-99
    • Ganti, V.1    Gehrke, J.2    Ramakrishnan, R.3
  • 9
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    • Discovery of multiple-level association rules from large databases
    • Han, J. and Fu, Y. "Discovery of multiple-level association rules from large databases." VLDB-95.
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    • Han, J.1    Fu, Y.2
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    • Principles for Mining Summaries Using Objective Measures of Interestingness
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    • Multi-level organization and summarization of the discovered rules
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    • KDD-2000
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  • 14
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    • Pruning and summarizing the discovered associations
    • Liu, B. Hsu, W. and Ma, Y. "Pruning and summarizing the discovered associations." KDD-99.
    • KDD-99
    • Liu, B.1    Hsu, W.2    Ma, Y.3
  • 16
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    • Efficient search of reliable exceptions
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    • Lu, H.1    Feng, F.2    Hussain, F.3
  • 18
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    • Megiddo, M. and Srikant, R. "Discovering predictive association rules." KDD-98, 1998.
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    • Ng, R.1    Lakshmanan, L.2    Han, J.3
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    • A belief-driven method for discovering unexpected patterns
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    • Peculiarity Oriented Mining and Its Application for Knowledge Discovery in Amino-acid Data
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    • Zhong, N.1    Ohshima, M.2    Ohsuga, S.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.