Fuzzy C-means and fuzzy TLBO for fuzzy clustering

Abstract

The choice of initial center plays a great role in achieving optimal clustering results in all partitional clustering approaches. Fuzzy C-means is a widely used approach but it also gets trapped in local optima values due to sensitiveness to initial cluster centers. To alleviate this issue, a new approach of using an evolutionary technique known as Teaching–Learning-Based Optimization (TLBO) is used hybridized with fuzzy approach. The proposed approach is able to deal with the sensitiveness of cluster centers. Results presented are very encouraging.