Enhancing K-means Clustering Algorithm with Improved Initial Center
Madhu Yedla, Srinivasa Rao, Pathakota, T M Srinivasa · 2010
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 method in data mining, due to its performance in clustering massive data sets. The final clustering result of the k- means clustering algorithm greatly depends upon the correctness of the initial centroids, which are selected randomly. The original k-means algorithm converges to local 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 of data 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 time comparatively original k-means clustering algorithm.