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
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;
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
DOI :
10.1109/BIBE.2007.4375732
