A Vibration Method for Discovering Density Varied Clusters. (15th November 2011)

• Record Type: Journal Article
• Authors: Elbatta, Mohammad T.; Bolbol, Raed M.; Ashour, Wesam M.
• Other Names: He Z. Academic Editor.; Hernandez J. A. Academic Editor.
• Is Part Of: ISRN artificial intelligence. Volume 2012(2012)
• Publication Date: 2011-11-15
• Subjects: Artificial intelligence -- Periodicals; Artificial intelligence; Periodicals; 006.3
• DOI: 10.5402/2012/723516
• Languages: English
• ISSNs: 2090-7435

Abstract

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.