Sine Cosine Based Algorithm for Data Clustering

Abstract

K-Means clustering algorithm is simple and prevalent, but it has a basic problem to stuck at local optima which relies on randomly generated centroid positions. Optimization algorithms are outstanding for their capacity to lead iterative computation in looking for global optima. Clustering analysis, in today�s world, is an important tool and seeking to recognize homogeneous groups of objects on the basis of values of attributes in many fields and applications. In this paper we have proposed a Sine Cosine based algorithm for data clustering (SCBAFDC). The proposed algorithm is tested on five benchmark datasets and compared with other five clustering algorithms. The results show that the proposed algorithm is giving competitive results as compared to the other algorithms in terms of quality of clustering.