Application of Teaching-Learning-Based Optimization Algorithm on Cluster Analysis

Cluster analysis has received attention in many scientific fields. The purpose of clustering analysis is to detect group data points, which are close to one another. One of the most widely used techniques for clustering is the K-means algorithm. The performance of K-means algorithm which converges to numerous local minima depends highly on initial cluster centers. In order to overcome local optima problem lots of studies done in clustering. A population-based method called Teaching-Learning-Based-Optimization (TLBO) is proposed to solve the clustering problem. TLBO is a robust and effective search algorithm. The most salient advantage of this algorithm is that it does not require the tuning of any kind of controlling parameters. The efficiency of the proposed algorithm is studied by testing on several data sets. Numerical results show that the proposed evolutionary optimization algorithm is robust and suitable for data clustering.