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Mathematical Problems in Engineering
Volume 2015, Article ID 187053, 9 pages
http://dx.doi.org/10.1155/2015/187053
Research Article

Bayesian-OverDBC: A Bayesian Density-Based Approach for Modeling Overlapping Clusters

1Department of Computer Engineering, Golpayegan University of Technology, Isfahan 87717-65651, Iran
2Department of Software Engineering, Faculty of Computer Engineering, University of Isfahan, Isfahan 81746-73441, Iran
3Department of Bio-Medical Engineering, University of Isfahan, Isfahan 81746-73441, Iran

Received 18 March 2015; Revised 14 June 2015; Accepted 21 October 2015

Academic Editor: Huaguang Zhang

Copyright © 2015 Mansooreh Mirzaie 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

Although most research in density-based clustering algorithms focused on finding distinct clusters, many real-world applications (such as gene functions in a gene regulatory network) have inherently overlapping clusters. Even with overlapping features, density-based clustering methods do not define a probabilistic model of data. Therefore, it is hard to determine how “good” clustering, predicting, and clustering new data into existing clusters are. Therefore, a probability model for overlap density-based clustering is a critical need for large data analysis. In this paper, a new Bayesian density-based method (Bayesian-OverDBC) for modeling the overlapping clusters is presented. Bayesian-OverDBC can predict the formation of a new cluster. It can also predict the overlapping of cluster with existing clusters. Bayesian-OverDBC has been compared with other algorithms (nonoverlapping and overlapping models). The results show that Bayesian-OverDBC can be significantly better than other methods in analyzing microarray data.