Bayesian-OverDBC: a Bayesian density-based approach for modeling overlapping clusters
type: Item
instance of: scholarly article
title: Bayesian-OverDBC: a Bayesian density-based approach for modeling overlapping clusters
zbMATH Open document ID: 1395.62167
DOI: 10.1155/2015/187053
author: Mansooreh Mirzaie
author: Ahmad Barani
author: Naser Nematbakkhsh
author: Majid Mohammad-Beigi
published in: Mathematical Problems in Engineering
publication date: 2018-08-27T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 62H30
Mathematics Subject Classification ID: 62C10
zbMATH DE Number: 6925706
Wikidata QID: Q59117529
describes a project that uses: DENCLUE
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2015/187053
OpenAlex ID: W2195533877
cites work: The Construction of Hierarchic and Non-Hierarchic Classifications
cites work: Q4723060
cites work: Q5456732
cites work: Q4139463
cites work: Advances in intelligent data analysis VII. 7th international symposium on intelligent data analysis, IDA 2007, Ljubljana, Slovenia, September 6--8, 2007. Proceedings
cites work: Extremal Combinatorics
cites work: Q3655274
cites work: Q5396636
description: scientific article; zbMATH DE number 6925706