期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2015
卷号：82
期号：3
出版社：Journal of Theoretical and Applied
摘要：Clustering over protein or gene data is now a popular issue in biomedical databases. In general large set of gene tags are clustered using high computation techniques over gene or protein distributed data. Most of the traditional clustering techniques based on subspace, hierarchical and partitioning feature extraction. Various clustering techniques have been proposed in the literature with different cluster measures, but the performance is limited due to its spatial noise and uncertainty. In this paper, an improved graph based clustering technique was proposed to generate efficient gene or protein clusters over uncertain and noisy data. Proposed graph based visualization can effectively identify different types of genes or proteins along with relational attributes. Experimental results show proposed graph model effectively clusters the complex gene or protein data compare to conventional clustering approaches.
