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Hybrid TRS-FA Clustering Approach for Web2.0 Social Tagging System

Hybrid TRS-FA Clustering Approach for Web2.0 Social Tagging System

Hannah Inbarani H, Selva Kumar S
Copyright: © 2015 |Volume: 2 |Issue: 1 |Pages: 18
ISSN: 2334-4598|EISSN: 2334-4601|EISBN13: 9781466680777|DOI: 10.4018/ijrsda.2015010105
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MLA

Inbarani H, Hannah, and Selva Kumar S. "Hybrid TRS-FA Clustering Approach for Web2.0 Social Tagging System." IJRSDA vol.2, no.1 2015: pp.70-87. http://doi.org/10.4018/ijrsda.2015010105

APA

Inbarani H, H. & Kumar S, S. (2015). Hybrid TRS-FA Clustering Approach for Web2.0 Social Tagging System. International Journal of Rough Sets and Data Analysis (IJRSDA), 2(1), 70-87. http://doi.org/10.4018/ijrsda.2015010105

Chicago

Inbarani H, Hannah, and Selva Kumar S. "Hybrid TRS-FA Clustering Approach for Web2.0 Social Tagging System," International Journal of Rough Sets and Data Analysis (IJRSDA) 2, no.1: 70-87. http://doi.org/10.4018/ijrsda.2015010105

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Abstract

Social tagging is one of the vital attributes of WEB2.0. The challenge of Web 2.0 is a gigantic measure of information created over a brief time. Tags are broadly used to interpret and arrange the web 2.0 assets. Tag clustering is the procedure of grouping the comparable tags into clusters. The tag clustering is extremely valuable for researching and organizing the web2. 0 resources furthermore critical for the achievement of Social Bookmarking frameworks. In this paper, the authors proposed a hybrid Tolerance Rough Set Based Firefly (TRS-Firefly-K-Means) clustering algorithm for clustering tags in social systems. At that stage, the proposed system is contrasted with the benchmark algorithm K-Means clustering and Particle Swarm optimization (PSO) based Clustering technique. The experimental analysis outlines the viability of the suggested methodology.

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