Data clustering based on hybrid K-harmonic means and modifier imperialist competitive algorithm

Clustering is a process for partitioning datasets. Clustering is one of the most commonly used techniques in data mining and is very useful for optimum solution. K-means is one of the simplest and the most popular methods that is based on square error criterion. This algorithm depends on initial states and is easily trapped and converges to local optima. Some recent researches show that K-means algorithm has been successfully applied to combinatorial optimization problems for clustering. K-harmonic means clustering solves the problem of initialization using a built-in boosting function, but it is suffering from running into local optima. In this article, we purpose a novel method that is based on combining two algorithms; K-harmonic means and modifier imperialist competitive algorithm. It is named ICAKHM. To carry out this experiment, four real datasets have been employed whose results indicate that ICAKHM. Four real datasets are employed to measure the proposed method include Iris, Wine, Glass and Contraceptive Method Choice with small, medium and large dimensions. The experimented results show that the new method (ICAKHM) carries out better results than the efficiency of KHM, PSOKHM, GSOKHM and ICAKM methods.