Hybrid Tolerance Rough Set Based Intelligent Approaches for Social Tagging Systems

Abstract

The major challenge with Big Data analysis is the generation of huge amounts of data over a short period like Social tagging system. Social Tagging systems such as BibSonomy and del.icio.us have become progressively popular with the widespread use of the internet. The social tagging system is a popular way to annotate web 2.0 resources. Social tagging systems allow users to annotate web resources with free-form tags. 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 tagging systems. Clustering the tag data is very tedious since the tag space is very large in several social bookmarking websites. So, instead of clustering the entire tag space of Web 2.0 data, some tags frequent enough in the tag space can be selected for clustering by applying feature selection techniques. The goal of feature selection is to determine a marginal bookmarked URL subset from Web 2.0 data while retaining a suitably high accuracy in representing the original bookmarks. In this chapter, Unsupervised Quick Reduct feature selection algorithm is applied to find a set of most commonly tagged bookmarks and this paper proposes TRS approach hybridized with Meta heuristic clustering algorithms. The proposed approaches are Hybrid TRS and K-Means Clustering (TRS-K-Means), Hybrid TRS and Particle swarm optimization (PSO) K-Means clustering algorithm (TRS-PSO-KMeans), and Hybrid TRS-PSO-K-Means-Genetic Algorithm (TRS-PSO-GA). These intelligent approaches automatically determine the number of clusters. These are in turn compared with K-Means benchmark algorithm for Social Tagging System.KeywordsTag ClusteringBookmark SelectionK-MeansTolerance Rough Set (TRS)Particle Swarm Optimization (PSO)Genetic Algorithm (GA)