Rough set-based meta-heuristic clustering approach for social e-learning systems
S. Selva Kumar, H. Hannah Inbarani, Ahmad Taher Azar, Aboul Alla Hassanien · International Journal of Intelligent Engineering Informatics · 2015
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.