Enhancing the K-means Clustering Algorithm by Using a O(n logn) Heuristic Method for Finding Better Initial Centroids

With the advent of modern techniques for scientific data collection, large quantities of data are getting accumulated at various databases. Systematic data analysis methods are necessary to extract useful information from rapidly growing data banks. Cluster analysis is one of the major data mining methods and the k-means clustering algorithm is widely used for many practical applications. But the original k-means algorithm is computationally expensive and the quality of the resulting clusters substantially relies on the choice of initial centroids. Several methods have been proposed in the literature for improving the performance of the k-means algorithm. This paper proposes an improvement on the classic k-means algorithm to produce more accurate clusters. The proposed algorithm comprises of a O(n logn) heuristic method, based on sorting and partitioning the input data, for finding the initial centroids in accordance with the data distribution. Experimental results show that the proposed algorithm produces better clusters in less computation time.