K-means based data stream clustering algorithm extended with no. of cluster estimation method

Abstract

: Data stream are generated from many sources. This Data streams are needed to be
transformed into significant information to take more effective decisions. Clustering is the best way
for analyzing data streams. The material on clustering is very large.Many clustering algorithms are
available for data stream which uses k-means algorithm as a base. Clustream algorithm is one of the
examples of it. Main drawback of such k-means based data stream clustering algorithm (Clustream) is
that user has to give no. of cluster (k) in advance. Many times it happens that user does not know
detail about the data and gives value of k randomly. In this type of case we will not get satisfactory
result. i.e. we can’t get proper quality of clusters. To tackle the above mentioned problem, we have
proposed the framework. According to it, we will use another algorithm to find appropriate no. of
clusters in advance. Here we used Bisecting k-means algorithm to find no. of clusters for data stream.
So we have combined the clustream algorithm with bisecting approach for finding best quality
clusters without interference of user to fix value of no. of cluster at user side.