Partition Based Clustering Using Genetic Algorithm and Teaching Learning Based Optimization: Performance Analysis

Clustering is useful in several machine learning and data mining applications such as information retrieval, market analysis, web analysis etc. The most popular partitioning clustering algorithms are K-means. The performance of these algorithms converges to local minima depends highly on initial cluster centroids. In order to overcome local minima apply evolutionary and population based methods like Genetic Algorithms and Teaching Learning Based Optimization (TLBO). The GA and TLBO is applied to solve the challenges of partitioning clustering method K-means like initial centroid selection and compare the results with using GA and TLBO. TLBO is latest population based method and uses a population of solutions to proceed to the global solutions and overcome the local minima problem. TLBO method is based on effect of the influence of a teacher on the output of learners in a class.