Combined elephant herding optimization algorithm with k-means for data clustering

Clustering is an important task in machine learning and data mining. Due to various applications that use clustering, numerous clustering methods were proposed. One well-known, simple, and widely used clustering algorithm is k-means. The main problem of this algorithm is its tendency of getting trapped into local minimum because it does not have any kind of global search. Clustering is a hard optimization problem, and swarm intelligence stochastic optimization algorithms are proved to be successful for such tasks. In this paper, we propose recent swarm intelligence elephant herding optimization algorithm for data clustering. Local search of the elephant herding optimization algorithm was improved by k-means. The proposed method was tested on six benchmark datasets and compared to other methods from literature. Based on the obtained results it can be concluded that the proposed method finds better clusters when silhouette score is used as the quality measure.