Research Article
Bayesian-OverDBC: A Bayesian Density-Based Approach for Modeling Overlapping Clusters
Baysian Overlapping Density Based Clustering Algorithm (OverDBC) | Input: Expression Matrix () , Data model | Output: Bayesian overlap clusters, (membership-matrix). | New cluster may be merged based on () probability. | //phase 1 | (1) compute transaction matrix () | //phase 2 | (2) Find Core genes based on Density and Closeness Centrality | (3) Add gene to Core genes () based on density and Cc relations. | //phase 3 | (4) For All in (Set of Core Object) Repeat: | (5) If Start Local Search to find nearest neighbors , Save cluster . | Else For to | | Based above probability select one of these paths: | if then Start local search to construct new cluster . | Else invoke func_bound_over( ) and return results | End of For | End of If |
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