Enhancing K-means Clustering Algorithm with Improved Initial Center

Cluster analysis is one of the primary data analysis methods and k-means is one of the most well known popular clustering algorithms. The k-means algorithm is one of the frequently used clustering methodin data mining, due to its performance in clustering massive data sets. The final clustering result of the kmeans clustering algorithm greatly depends upon the correctness of the initial centroids, which are selected randomly. The original k-means algorithm converges tolocal minimum, not the global optimum. Many improvements were already proposed to improve the performance of the k-means, but most of these require additional inputs like threshold values for the number ofdata points in a set. In this paper a new method is proposed for finding the better initial centroids and to provide an efficient way of assigning the data points to suitable clusters with reduced time complexity. According to our experimental results, the proposed algorithm has the more accuracy with less computational timecomparatively original k-means clustering algorithm.