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ISRN Artificial Intelligence
Volume 2012 (2012), Article ID 723516, 8 pages
http://dx.doi.org/10.5402/2012/723516
Research Article

A Vibration Method for Discovering Density Varied Clusters

Department of Computer Engineering, Islamic University of Gaza, Palestine

Received 4 August 2011; Accepted 28 August 2011

Academic Editors: Z. He and J. A. Hernandez

Copyright © 2012 Mohammad T. Elbatta et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

DBSCAN is a base algorithm for density-based clustering. It can find out the clusters of different shapes and sizes from a large amount of data, which is containing noise and outliers. However, it is fail to handle the local density variation that exists within the cluster. Thus, a good clustering method should allow a significant density variation within the cluster because, if we go for homogeneous clustering, a large number of smaller unimportant clusters may be generated. In this paper, an enhancement of DBSCAN algorithm is proposed, which detects the clusters of different shapes and sizes that differ in local density. Our proposed method VMDBSCAN first finds out the “core” of each cluster—clusters generated after applying DBSCAN. Then, it “vibrates” points toward the cluster that has the maximum influence on these points. Therefore, our proposed method can find the correct number of clusters.