A Kernel-Based Intuitionistic Fuzzy C-Means Clustering Using Improved Multi-Objective Immune Algorithm

Abstract

Clustering algorithms have attracted a lot of attentions recently in real-world applications. However, the traditional clustering algorithms still have plenty of defects which are not yet resolved. In this paper, a kernel-based intuitionistic fuzzy C-means clustering using improved multi-objective artificial immune algorithm (KIFCM-IMOIA) is proposed. In our algorithm, the kernel trick and the intuitionistic fuzzy entropy (IFE) are introduced into the objective functions, which improves the robustness to noises. In addition, an improved multi-objective optimization immune algorithm (IMOIA), which simultaneously optimizes the intra-cluster compactness and inter-cluster separation, is proposed to prevent the algorithm from falling into local optimum. The proposed IMOIA uses a novel active antibody selection strategy, a hybrid differential evolution strategy, and an adaptive mutation operator to maintain better distribution of the solutions with better convergence. Finally, we performed experiments using 14 UCI datasets and compared our algorithm with six clustering methods on three performance metrics. The experimental results show that our algorithm performs better than other algorithms.

Keywords

• Intuitionistic fuzzy C-means
• kernel function
• artificial immune algorithm
• multi-objective optimization