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

Mohammad Bozlul Karim, Nobutaka Wakamatsu, M. Altaf-Ul-Amin
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引用次数: 15

Abstract

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.
[献给T. Okada教授和T. Nishioka教授:化学中的数据科学]DPClusOST:通用图聚类的软件工具
由于计算机和通信技术的信息共享,现代世界正在整合高度连接的异构数据。这些数据导致了一种复杂的关系,需要向下钻取和挖掘才能理解数据的实际含义。今天,任何现代计算技术都使用图聚类作为一种复杂的数据分析技术。本文实现了一种操作简单、可视化技术清晰的广义图聚类算法DPClusO。DPClusO是DPClus算法的增强版本,它考虑了簇的重叠特性以及密度和外围跟踪。用户可以选择不同的参数和可视化属性来呈现聚类集、单聚类、分层图等,并将这些数据保存为图像和文本格式。本文以从KNApSAcK数据库中收集的代谢物网络为例,讨论了所提出的软件工具的一步一步操作。该工具成功地生成了结构相似的代谢物的内聚群。该工具可用于分析任何研究领域的网络数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Computer Aided Chemistry
Journal of Computer Aided Chemistry CHEMISTRY, MULTIDISCIPLINARY-
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