Improvement of Initial Cluster Center of C-means using Teaching Learning based Optimization

While clustering the data using fuzzy c-means (FCM) and hard c-means (HCM), the sensitivity to tune the initial clusters centers have captured the attention of the clustering communities for quite a long time. In this study, we have taken help of new evolutionary algorithm, Teaching learning based Optimization (TLBO), is proposed as a method to address this problem. The proposed approach consists of two stages. In the first stage, the TLBO explores the search space of given dataset to find out near-optimal cluster centers. The cluster centers found by TLBO are then evaluated using reformulated c-mean objective function. In the second stage, the best cluster centers found are used as the initial cluster center for the c-mean algorithms. Our experiments show that TLBO can minimize the difficulty of choosing an initialization for the c-means clustering algorithms. For purposes of evaluation, standard benchmark data and artificial data are experimented.