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Volumn , Issue , 2007, Pages 18-36

Raising, to enhance rule mining in Web marketing with the use of an ontology

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EID: 62449296138     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4018/978-1-59904-618-1.ch002     Document Type: Chapter
Times cited : (13)

References (32)
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    • Berzal, F.1    Blanco, I.2    Sanchez, D.3    Vila, M.-A.4
  • 9
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    • Raising data for improved support in rule mining: How to raise and how far to raise
    • Geller, J., Zhou, X., Prathipati, K., Kanigiluppai, S., & Chen, X. (2005). Raising data for improved support in rule mining: How to raise and how far to raise. Intelligent Data Analysis, 9(4), 397-415.
    • (2005) Intelligent Data Analysis , vol.9 , Issue.4 , pp. 397-415
    • Geller, J.1    Zhou, X.2    Prathipati, K.3    Kanigiluppai, S.4    Chen, X.5
  • 13
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    • Mining frequent patterns without candidate generation: A frequent-pattern tree approach
    • Han, J., Pei, J., Yin, Y., & Mao, R. (2004). Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Mining and Knowledge Discovery, 8(1), 53-87.
    • (2004) Data Mining and Knowledge Discovery , vol.8 , Issue.1 , pp. 53-87
    • Han, J.1    Pei, J.2    Yin, Y.3    Mao, R.4
  • 14
    • 33644650821 scopus 로고    scopus 로고
    • Mining ontology for automatically acquiring Web user information needs
    • Li, Y., & Zhong, N. (2006). Mining ontology for automatically acquiring Web user information needs. IEEE Transactions on Knowledge and Data Engineering, 18(4), 554-568.
    • (2006) IEEE Transactions On Knowledge and Data Engineering , vol.18 , Issue.4 , pp. 554-568
    • Li, Y.1    Zhong, N.2
  • 15
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    • Mining weighted association rules
    • Lu, S., Hu, H., & Li, F. (2001). Mining weighted association rules. Intelligent Data Analysis, 5(3), 211-225.
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    • Lu, S.1    Hu, H.2    Li, F.3
  • 18
    • 0037687951 scopus 로고    scopus 로고
    • Text mining techniques to automatically enrich a domain ontology
    • Missikoff, M., Velardi, P., & Fabriani, P. (2003). Text mining techniques to automatically enrich a domain ontology. Applied Intelligence, 18(3), 323-340.
    • (2003) Applied Intelligence , vol.18 , Issue.3 , pp. 323-340
    • Missikoff, M.1    Velardi, P.2    Fabriani, P.3
  • 25
    • 25144497596 scopus 로고    scopus 로고
    • Thesaurus or logical ontology, which one do we need for text mining?
    • Tsujii, J., & Ananiadou, S. (2005). Thesaurus or logical ontology, which one do we need for text mining? Language Resources and Evaluation, 39(1), 77-90.
    • (2005) Language Resources and Evaluation , vol.39 , Issue.1 , pp. 77-90
    • Tsujii, J.1    Ananiadou, S.2
  • 28
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    • Knowledge Management in Tourism
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    • Xodo, D., & Nigro, H.O. (2005). Knowledge Management in Tourism. In L.C. Rivero, J.H. Doorn, & V.E. Ferraggine (Eds.). Encyclopedia of database technologies and applications (pp. 319-329). Hershey, PA: Idea Group Reference.
    • (2005) Encyclopedia of Database Technologies and Applications , pp. 319-329
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  • 31
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    • Doctoral dissertation, New Jersey Institute of Technology, Newark, NJ
    • Zhou, X. (2006). Enhancing web marketing by using an ontology. Doctoral dissertation, New Jersey Institute of Technology, Newark, NJ.
    • (2006) Enhancing Web Marketing By Using An Ontology
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  • 32
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    • Rule mining with prior knowledge: A belief networks approach
    • Zhou, Z., Liu, H., Li, S.Z., & Chua, C.S. (2001). Rule mining with prior knowledge: A belief networks approach. Intelligent Data Analysis, 5(2), 95-110.
    • (2001) Intelligent Data Analysis , vol.5 , Issue.2 , pp. 95-110
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.