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논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2009.8
- 수록면
- 3,480 - 3,484 (5page)
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초록· 키워드
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Classification rule mining is apractical data mining technique widely used inreal world. In the previous work, we have put for ward a fuzzy classassociation rule mining method based on Genetic Network Programming and applied it to network in trusion detection system which proved it sefficiency and advantage. In this paper, a detailed comparison not only between fuzzy class association rule minings(FCARMs) with fixed fuzzy membership functions and with evolved fuzzy membership functions, but also between FCARMs with and with out probability no detransition are carried out. The aim of this paper is to provide experimental analysis on the characteristics of FCARMs with different implementations. Experimental results conducted on real world database, KDD99 Cup and DAPRA 98 database from MITL incoln Laboratory, are studied to verify the comparison.
목차
Abstract
1.INTRODUCTION
2.GENETIC NETWORK PROGRAMMING
3.FUZZY CLASS ASSOCIATION RULEMINING(FCARM)
4.COMPARISON OF FCARMS WITHD IFFERENT IMPLEMENTATIONS
5.SIMULATION RESULTS
6.CONCLUSION
REFERENCES
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