Evolutionary K-means Clustering Algorithm

Clustering techniques have received attention in many fields of study such as engineering, medicine, biology and data mining. The aim of clustering is to collect data points. The K-means algorithm is one of the most common techniques used for clustering. However, the results of K-means depend on the initial state and converge to local optima. In order to overcome local optima obstacles, a lot of studies have been done in clustering. This paper presents an efficient hybrid evolutionary optimization algorithm based on combining Modify gravitational search algorithm and K-means for optimum clustering N objects into K clusters. Experiments with 1 bench-mark datasets have shown similar or slightly better quality of the results compared to standard K-Means algorithm and other algorithm. The experiment results show that proposed algorithm clustering has not only higher accuracy but also higher level of stability. And the faster convergence speed can also be validated by statistical results.

کلیدواژه ها:

component: data clustering ، K-Means ، Evolutionary Algorithms