DBSCAN is a base algorithm for density-based clustering. It can find out the clusters of different shapes and sizes from a large\r\namount of data, which is containing noise and outliers. However, it is fail to handle the local density variation that exists within\r\nthe cluster. Thus, a good clustering method should allow a significant density variation within the cluster because, if we go for\r\nhomogeneous clustering, a large number of smaller unimportant clusters may be generated. In this paper, an enhancement of\r\nDBSCAN algorithm is proposed, which detects the clusters of different shapes and sizes that differ in local density. Our proposed\r\nmethod VMDBSCAN first finds out the ââ?¬Å?coreââ?¬Â of each clusterââ?¬â?clusters generated after applying DBSCAN. Then, it ââ?¬Å?vibratesââ?¬Â\r\npoints toward the cluster that has the maximum influence on these points. Therefore, our proposed method can find the correct\r\nnumber of clusters.
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