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Volumn 36, Issue 3 PART 2, 2009, Pages 6019-6024

Finding "persistent rules": Combining association and classification results

Author keywords

Association mining; Classification; Persistent rules; Strong rules

Indexed keywords

CLASSIFICATION (OF INFORMATION); INFORMATION SYSTEMS; MATHEMATICAL MODELS;

EID: 58349100458     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2008.06.090     Document Type: Article
Times cited : (11)

References (12)
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    • American National Election Studies (ANES). (2005) Center for political studies. Ann Arbor, MI: University of Michigan.
    • American National Election Studies (ANES). (2005) Center for political studies. Ann Arbor, MI: University of Michigan.
  • 3
    • 0037664299 scopus 로고    scopus 로고
    • Deshpande, M., & Karypis, G. (2002). Using conjunction of attribute values for classification. In Proceedings of the eleventh international conference on information and knowledge management, McLean, VA, pp. 356-364.
    • Deshpande, M., & Karypis, G. (2002). Using conjunction of attribute values for classification. In Proceedings of the eleventh international conference on information and knowledge management, McLean, VA, pp. 356-364.
  • 4
    • 58349108623 scopus 로고    scopus 로고
    • Edsall, T. B. (2006). Democrats' data mining stirs an intraparty battle. The Washington Post, March 8: A1.
    • Edsall, T. B. (2006). Democrats' data mining stirs an intraparty battle. The Washington Post, March 8: A1.
  • 5
    • 33744467993 scopus 로고    scopus 로고
    • Data dimensionality reduction with application to improving classification performance and explaining concepts of data sets
    • Fu X., and Wang L. Data dimensionality reduction with application to improving classification performance and explaining concepts of data sets. International Journal of Business Intelligence and Data Mining 1 1 (2005) 65-87
    • (2005) International Journal of Business Intelligence and Data Mining , vol.1 , Issue.1 , pp. 65-87
    • Fu, X.1    Wang, L.2
  • 7
    • 12244313033 scopus 로고    scopus 로고
    • Jaroszewicz, S., & Simovici, D. A. (2004). Interestingness of frequent itemsets using Bayesian networks as background knowledge. In Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, Seattle, WA, pp. 178-186.
    • Jaroszewicz, S., & Simovici, D. A. (2004). Interestingness of frequent itemsets using Bayesian networks as background knowledge. In Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, Seattle, WA, pp. 178-186.
  • 8
    • 58349088431 scopus 로고    scopus 로고
    • Murray, G.R., & Scime, A. (in press). Micro-targeting and electorate segmentation: Data mining the American national election studies. Journal of Political Marketing.
    • Murray, G.R., & Scime, A. (in press). Micro-targeting and electorate segmentation: Data mining the American national election studies. Journal of Political Marketing.
  • 9
    • 58349117169 scopus 로고    scopus 로고
    • Murray, G. R., Riley, C., & Scime, A. (2007). "A new age solution for an age-old problem: Mining data for likely voters, presented at the 62nd annual conference of the american association of public opinion research, May 17-20, 2007, Anaheim, CA.
    • Murray, G. R., Riley, C., & Scime, A. (2007). "A new age solution for an age-old problem: Mining data for likely voters, presented at the 62nd annual conference of the american association of public opinion research, May 17-20, 2007, Anaheim, CA.
  • 10
    • 0034593047 scopus 로고    scopus 로고
    • Padmanabhan, B., & Tuzhilin, A. (2000). Small is beautiful: discovering the minimal set of unexpected patterns. In Proceedings of the sixth ACM SIGKDD international conference on knowledge discovery and data mining, Boston, MA, pp. 54-63.
    • Padmanabhan, B., & Tuzhilin, A. (2000). Small is beautiful: discovering the minimal set of unexpected patterns. In Proceedings of the sixth ACM SIGKDD international conference on knowledge discovery and data mining, Boston, MA, pp. 54-63.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.