A Model of Immune Gene Expression Programming for Rule Mining

Tao Zeng (Sichuan University, China)

Changjie Tang (Sichuan University, China)

Yong Xiang (Chengdu Electromechanical College, China)

Peng Chen (Sichuan University, China)

Yintian Liu (Sichuan University, China)

Abstract: Rule mining is an important issue in data mining. To address it, a novel Immune Gene Expression Programming (IGEP) model was proposed. Concepts of rule, gene, immune cell, and antibody were formalized. The dynamic evolution models and the corresponding recursive equations of immune cell, self, immune-tolerance were built. The novel key techniques of IGEP were presented. Experiment results showed that the new method has good stability, scalability and flexibility. It can discover traditional association rule, non-traditional rule including connective "OR" or "NOT", and meta-rule of strong rule. Furthermore, it can perform well in constrained pattern mining.

Keywords: artifical immune system, data mining, evolutionary algorithm, gene expression programming, meta-rule, rule

Categories: F.2.2, H.2.8, I.2.6, I.5.2, I.6.5, M.7