A Refined K-Means Technique to Find the Frequent Item Sets

Abstract

In this paper we have shown the behaviour of the new k-means algorithm. In k-means clustering first we take the ‘n’ number of item sets, then we group those item sets into the k clusters by placing the item set in the cluster with nearest mean. The traditional k-means clustering is completely depend on initial clusters and can be used only on spherical-shape clusters. The traditional k-means clustering uses the euclidean distance but in our paper we have replaced it with minkowski distance and combined with the Generalized Sequential Pattern algorithm (GSP algorithm) to find the frequent item sets in the sequential data stream. The GSP algorithm based on the frequent item sets, it traces the databases iteratively. The modified k-means clustering have reduce the complexity and calculations and the GSP algorithm has given the better result than any other algorithm to find the frequent item sets. The results show that this approach has given the better performance when compared to the traditional k means clustering.