Fuzzy data clustering using artificial immune network

Authors:
Li Liu;Wenbo Xu
Affiliations:
School of Information Technology, Southern Yangtze University, Wuxi, Jiangsu, China;School of Information Technology, Southern Yangtze University, Wuxi, Jiangsu, China
Venue:
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Year:
2006

Citing 3
Cited 0

Unsupervised Optimal Fuzzy Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
Validity-guided (re)clustering with applications to image segmentation

IEEE Transactions on Fuzzy Systems

Quantified Score

Hi-index	0.00

Visualization

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.