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

Abstract EN

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.