Fuzzy Data Clustering Using Artificial Immune Network

Abstract

This paper presents a novel fuzzy clustering method named as AINFCM, which solves the traditional fuzzy clustering problems by searching for the optimal centroids of clusters using artificial immune network technology. Based on the clone and affinity mutation principals of biological immunity mechanism, containing memory cells, the AINFCM is capability of maintaining local optima solutions and exploring the global optima defined as minimum of the objective function. The algorithm is described theoretically and compared with classical K-means, K-medoid, FCM and GK Clustering methods using PC, CE, SC, S, ADI and DI validity indexes. Parameter setting was also discussed to analyze how sensitive the AINFCM is to user-defined parameters.