Empirical Study on Clustering Based on Modified Teaching Learning Based Optimization

In this Paper the focus is given on data clustering using Modified Teaching–Learning Based Optimization (MTLBO) a hybridization technique of TLBO. Unlike TLBO, this population based method works on the effect of influence of a teacher on learners to find the optimum solution and it has been used for clustering. The motivation behind the data clustering is to find inherent structure in the data items and grouping then on the basis of their mutual similarity. The effectiveness of the method is tested on many benchmark problems with different characteristics and the results are compared with other population based methods and finally it is implemented on clustering using neural network in data mining.