A Bayesian Framework for Improving Clustering Accuracy of Protein Sequences Based on Association Rules

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Author(s): Peng-Yeng Yin (National Chi University, Taiwan), Shyong-Jian Shyu (Ming Chuan University, Taiwan), Guan-Shieng Huang (National Chi University, Taiwan)and Shuang-Te Liao (Ming Chuan University, Taiwan)
Copyright: 2008
Pages: 12
Source title: Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-59904-951-9.ch062

Keywords: Data Mining and Databases / Data Warehousing / Information Science Reference / Library & Information Science

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Abstract

With the advent of new sequencing technology for biological data, the number of sequenced proteins stored in public databases has become an explosion. The structural, functional, and phylogenetic analyses of proteins would benefit from exploring databases by using data mining techniques. Clustering algorithms can assign proteins into clusters such that proteins in the same cluster are more similar in homology than those in different clusters. This procedure not only simplifies the analysis task but also enhances the accuracy of the results. Most of the existing protein-clustering algorithms compute the similarity between proteins based on one-to-one pairwise sequence

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A Bayesian Framework for Improving Clustering Accuracy of Protein Sequences Based on Association Rules

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