DocumentCode
1992427
Title
An Algorithm for Mining Fuzzy Association Rules Based on Immune Principles
Author
Zhang Lei ; Li Ren-hou
Author_Institution
Xi´an Jiaotong Univ., Xi´an
fYear
2007
fDate
14-17 Oct. 2007
Firstpage
1285
Lastpage
1289
Abstract
In this paper, an algorithm was proposed for mining fuzzy association rules based on natural immune principles. The proposed algorithm is mainly inspired by the clonal selection principle of biological immune systems. It was employed to optimize the number of fuzzy association rules that satisfy the specified thresholds by adjusting the parameters of fuzzy sets for each quantitative attribute. The performance of our algorithm has been compared with other relevant algorithms and the experimental results showed the effectiveness of our algorithm.
Keywords
artificial immune systems; data mining; fuzzy set theory; algorithm; biological immune systems; clonal selection principle; data mining; fuzzy association rules; fuzzy sets; natural immune principles; Association rules; Clustering algorithms; Data mining; Fuzzy sets; Fuzzy systems; Humans; Immune system; Relational databases; Systems engineering and theory; Transaction databases; association rules; data mining; fuzzy sets; immune principles;
fLanguage
English
Publisher
ieee
Conference_Titel
Bioinformatics and Bioengineering, 2007. BIBE 2007. Proceedings of the 7th IEEE International Conference on
Conference_Location
Boston, MA
Print_ISBN
978-1-4244-1509-0
Type
conf
DOI
10.1109/BIBE.2007.4375732
Filename
4375732
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