[Dedicated to Prof. T. Okada and Prof. T. Nishioka: data science in chemistry]DPClusOST: A Software Tool for General Purpose Graph Clustering

Modern world is incorporating highly connected heterogeneous data due to information sharing through computer and communication technology. These data lead to a complex relation where drilling down and mining are needed for understanding the actual meaning of data. Today any modern computational tec...

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Bibliographic Details
Published in:Journal of Computer Aided Chemistry 2017, Vol.18, pp.76-93
Main Authors: Karim, Mohammad Bozlul, Wakamatsu, Nobutaka, Altaf-Ul-Amin, Md
Format: Article
Language:English
Subjects:
Algorithms
Cluster
cluster property
Clustering
Data analysis
Data science
density
metabolite
Metabolites
Network analysis
Organic chemistry
overlapping coefficient
Software
Software development tools
Technology assessment
Visualization
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Summary:Modern world is incorporating highly connected heterogeneous data due to information sharing through computer and communication technology. These data lead to a complex relation where drilling down and mining are needed for understanding the actual meaning of data. Today any modern computational technique uses graph clustering as a sophisticated technology for data analysis. In this paper we implement a generalized graph clustering algorithm DPClusO with easy operating procedure and clear visualization techniques. DPClusO is enhanced version of DPClus algorithm where overlapping property of clusters is taken into consideration along with density and periphery tracking. User can select different parameters and visualization attributes to render cluster set, single cluster, hierarchical graph etc. and save these data in image and text formats. This paper discusses step by step operation of the proposed software tool using an example network of metabolites collected from KNApSAcK database. This tool successfully generated cohesive groups of structurally similar metabolites. The tool can be used for analysis of network data of any field of studies.
ISSN:1345-8647
1345-8647
DOI:10.2751/jcac.18.76