Hybrid Clustering using Elitist Teaching Learning-Based Optimization

Abstract

Data clustering is a key field of research in the pattern recognition arena. Although clustering is an unsupervised learning technique, numerous efforts have been made in both hard and soft clustering. In hard clustering, K-means is the most popular method and is being used in diversified application areas. In this paper, an effort has been made with a recently developed population based metaheuristic called Elitist based teaching learning based optimization (ETLBO) for data clustering. The ETLBO has been hybridized with K-means algorithm (ETLBO-K-means) to get the optimal cluster centers and effective fitness values. The performance of the proposed method has been compared with other techniques by considering standard benchmark real life datasets as well as some synthetic datasets. Simulation and comparison results demonstrate the effectiveness and efficiency of the proposed method.