KMGEM: Data Clustering by Combination of K-Means and Grenade Explosion Algorithm
Parvin Ghaffarzadeh, H. Mohammad, Akbar Nabiollahi · International Journal of Computer Applications · 2016
The main purpose of using clustering techniques is to divide a dataset into a few unsupervised data analysis partitions.One of the recent and apparently one of the easiest one of them is k-means.This technique is based on square error criterion.To solve the combinatorial optimization issues in the context of clustering techniques, k-means algorithm was used recently.In spite of the fact that it has been applied to a few territories, it experiences sensitivity to initial points.There have been a few techniques that were reported beneficial for improving kmeans systems.By this paper we are trying to suggest a new algorithm which depends on an optimized clustering method.This algorithm that is called K-Means Modified Grenade Explosion Method (KMGEM) is a K-Means that initialized with Modified Grenade Explosion algorithm.The results showed that our proposed method is superior in comparison with methods like Genetic Algorithm, Genetic K-Means Algorithm, and k-means algorithms.