Mining class association rules with artificial immune system

Authors:
Tien Dung Do;Siu Cheung Hui;Alvis C. M. Fong
Affiliations:
School of Computer Engineering, Nanyang Technological University, Singapore;School of Computer Engineering, Nanyang Technological University, Singapore;School of Computer Engineering, Nanyang Technological University, Singapore
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
Year:
2005

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Abstract

Associative classification, which is based on association rules, has shown great promise over many other classification techniques. However, the very large search space of possible rules may cause performance degradation in the rule mining process as well as classification accuracy. In this paper, we propose a new approach known as AIS-AC, which is based on Artificial Immune System (AIS), for mining class association rules for associative classification. Instead of massively searching for all possible association rules, AIS-AC will only find a subset of association rulesthat are suitable for effective associative classification in an evolutionary manner.