Dynamic Kernel Clustering by Spider Monkey Optimization Algorithm

In data, analysis clustering plays a major role. In the past decade varieties of clustering algorithms are proposed and produced better results. But many of them required prior information on the number of clusters and failed to produce optimum results when such information is not available. In real-life problems, it is difficult to predict the number of clusters due to the complexity of data in shape and dimensionality. Therefore predicting the number of clusters is a difficult task and this draws the attention of many researchers. In this work, we proposed DKCSMO, dynamic kernel clustering with a spider monkey optimization algorithm. In this work for better clustering results, the local leader phase of the spider monkey optimization algorithm is improved with the neighborhood search strategy. Further to improve the quality of results, we modified CS-Index with Gaussian kernel distribution. The proposed algorithm is compared with five well-known meta-heuristic algorithms and seven previously published automatic clustering algorithms. Experimental results show that the proposed algorithm produced better results in terms of the predicted clusters, DB, SIL, and ARI measures.