Rough set-based meta-heuristic clustering approach for social e-learning systems. (2015)

• Record Type: Journal Article
• Authors: Kumar, S. Selva; Inbarani, H. Hannah; Azar, Ahmad Taher; Hassanien, Aboul Alla
• Is Part Of: International journal of intelligent engineering informatics. Volume 3: Number 1 (2015)
• Page Start: 23
• Page End: 41
• Publication Date: 2015
• Subjects: clustering tags -- k-means clustering -- tolerance rough sets -- e-learning -- electronic learning -- online learning -- PSO clustering -- web 20 -- particle swarm optimisation -- social tagging; Artificial intelligence -- Engineering applications -- Periodicals; Engineering -- Computer programs -- Periodicals; Knowledge management -- Periodicals; 620.0028563
• Languages: English
• ISSNs: 1758-8715

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.