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Hybrid TRS-PSO Clustering Approach for Web2.0 Social Tagging System
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Author(s): Hannah Inbarani H (Department of Computer Science, Periyar University, Salem, India), Selva Kumar S (Department of Computer Science, Periyar University, Salem, India), Ahmad Taher Azar (Benha University, Benha, Egypt) and Aboul Ella Hassanien (Cairo University, Cairo, Egypt, & Computers and Information Faculty, Beni Suef University, Beni Suef, Egypt, & Scientific Research Group in Egypt (SRGE), Giza, Egypt)
Copyright: 2015
Volume: 2
Issue: 1
Pages: 16
Source title:
International Journal of Rough Sets and Data Analysis (IJRSDA)
Editor(s)-in-Chief: Parikshit Narendra Mahalle (Department of Artificial Intelligence and Data Science, Bansilal Ramnath Agarwal Charitable Trust's, Vishwakarma Institute of Information Technology, India)
DOI: 10.4018/ijrsda.2015010102
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Abstract
Social tagging is one of the important characteristics of WEB2.0. The challenge of Web 2.0 is a huge amount of data generated over a short period. Tags are widely used to interpret and classify the web 2.0 resources. Tag clustering is the process of grouping the similar tags into clusters. The tag clustering is very useful for searching and organizing the web2.0 resources and also important for the success of Social Bookmarking systems. In this paper, the authors proposed a hybrid Tolerance Rough Set Based Particle Swarm optimization (TRS-PSO) clustering algorithm for clustering tags in social systems. Then the proposed method is compared to the benchmark algorithm K-Means clustering and Particle Swarm optimization (PSO) based Clustering technique. The experimental analysis illustrates the effectiveness of the proposed approach.
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