Title: Rough set-based meta-heuristic clustering approach for social e-learning systems

Authors: S. Selva Kumar; H. Hannah Inbarani; Ahmad Taher Azar; Aboul Ella Hassanien

Addresses: Department of Computer Science, Periyar University, Salem 636011, Tamil Nadu, India ' Department of Computer Science, Periyar University, Salem 636011, Tamil Nadu, India ' Faculty of Computers and Information, Benha University, Benha, Egypt ' Faculty of Computer and Information, Cairo University, Cairo, Egypt

Abstract: An imperative challenge of Web 2.0 is the way that an incredible measure of information has been incited over a brief time. Tags are generally used to dig and arrange the Web 2.0 resources. Clustering the tag information is exceptionally dreary since the tag space is significant in a few social tagging sites. Tag clustering is the method of collecting the comparative tags into groups. The tag clustering is truly helpful for searching and arranging the Web 2.0 resources furthermore vital for the achievement of social tagging systems. In this paper, the clustering techniques apply to the social e-learning tagging system (http://www.pumrpelearning.com); furthermore, we proposed a hybrid tolerance rough set-based particle swarm optimisation (TRS-PSO) for clustering tags. At that stage, the proposed technique is contrasted with benchmark clustering algorithm k-means with particle swarm optimisation (PSO)-based grouping method. The exploratory investigation represents the character of the suggested methodology.

Keywords: clustering tags; k-means clustering; tolerance rough sets; e-learning; electronic learning; online learning; PSO clustering; web 2.0; particle swarm optimisation; social tagging.

DOI: 10.1504/IJIEI.2015.069098

International Journal of Intelligent Engineering Informatics, 2015 Vol.3 No.1, pp.23 - 41

Received: 12 Dec 2014
Accepted: 09 Jan 2015
Published online: 27 Apr 2015 *

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