A new algorithm for data clustering based on gravitational search algorithm and genetic operators

Abstract

Data clustering is a crucial technique in data mining that is used in many applications. In this paper, a new clustering algorithm based on gravitational search algorithm (GSA) and genetic operators is proposed. The local search solution is utilized throw the global search to avoid getting stock in local optima. The GSA is a new approach to solve optimization problem that inspired by Newtonian law of gravity. We compared the performances of the proposed method with some well-known clustering algorithms on five benchmark dataset from UCI Machine Learning Repository. The experimental results show that our approach outperforms other algorithms and has better solution in all datasets.