Modified K-Means for Better Initial Cluster Centres?

The k-means clustering algorithm is most popularly used in data mining for real world applications. The efficiency and performance of the k-means algorithm is greatly affected by initial cluster centers as different initial cluster centers often lead to different clustering. In this paper, we propose a modified k-means algorithm which has additional steps for selecting better cluster centers. We compute Min and Max distance for every cluster and find high density objects for selection of better k.