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Volumn 4, Issue 2, 2008, Pages 609-616

Mining quantitative association rules by interval clustering

Author keywords

Data mining; Interval clustering; Quantitative association rule

Indexed keywords

ASSOCIATION RULES; CLASSIFICATION (OF INFORMATION); CLUSTERING ALGORITHMS; DATA PROCESSING; INFORMATION ANALYSIS;

EID: 42549148944     PISSN: 15539105     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (10)

References (12)
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    • Agrawal, R.1    Imieliski, T.2    Swami, A.3
  • 2
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    • Mining generalized associations of semantic relations from textual web content
    • T. Jiang, A. Tan, K. Wang. Mining Generalized Associations of Semantic Relations from Textual Web Content. IEEE Transactions on Knowledge and Data Engineering, 19(2): 164-179, 2007.
    • (2007) IEEE Transactions on Knowledge and Data Engineering , vol.19 , Issue.2 , pp. 164-179
    • Jiang, T.1    Tan, A.2    Wang, K.3
  • 4
    • 34247107209 scopus 로고    scopus 로고
    • Mining association rules for adaptive search engine based on RDF technology
    • Y. Takama. S. Hattori. Mining Association Rules for Adaptive Search Engine Based on RDF Technology; IEEE Transactions on Industrial Electronics, 54(2): 790-796, 2007.
    • (2007) IEEE Transactions on Industrial Electronics , vol.54 , Issue.2 , pp. 790-796
    • Takama, Y.1    Hattori, S.2
  • 6
    • 35048872687 scopus 로고    scopus 로고
    • An efficient method for quantitative association rules to raise reliance of data
    • H. Lee, W. Park, and D. Park, An Efficient Method for Quantitative Association Rules to Raise Reliance of Data. In: APWeb 2004, 2004: 506-512.
    • (2004) APWeb 2004 , pp. 506-512
    • Lee, H.1    Park, W.2    Park, D.3
  • 7
    • 33644657610 scopus 로고    scopus 로고
    • Software defect association mining and defect correction effort prediction
    • Q. Song; M. Shepperd, M. Cartwright et al. Software defect association mining and defect correction effort prediction. IEEE Transactions on Software Engineering, 32(2): 69-82, 2006.
    • (2006) IEEE Transactions on Software Engineering , vol.32 , Issue.2 , pp. 69-82
    • Song, Q.1    Shepperd, M.2    Cartwright, M.3
  • 9
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    • Mining for strong negative associations in a large database of customer transactions
    • A. Savasere, E. Omiecinski, and S. Navathe, Mining for strong negative associations in a large database of customer transactions. In: Proceedings of ICDE. 1998: 494-502.
    • (1998) Proceedings of ICDE , pp. 494-502
    • Savasere, A.1    Omiecinski, E.2    Navathe, S.3
  • 10
    • 0031162287 scopus 로고    scopus 로고
    • Association rules over interval data
    • R. Miller, Y. Yang, Association Rules over Interval Data. In: Proceedings ACM SIGMOD97, 1997: 452-461.
    • (1997) Proceedings ACM SIGMOD97 , pp. 452-461
    • Miller, R.1    Yang, Y.2
  • 12
    • 0030157416 scopus 로고    scopus 로고
    • Mining quantitative association rules in large tables
    • R. Srikant and R. Agrawal, Mining Quantitative Association Rules in Large Tables. In: Proceedings of ACM SIGMOD, 1996: 1-12.
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    • Srikant, R.1    Agrawal, R.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.