Efficient Management of Community Question Answering Sites using Improved Spectral Clustering –

Community Question-Answering(CQA) sites are the major platform where posts are generated by peers in the form of questions and answers for information seeking in online environments. In general, multiple posts are created by different users on a particular topic or subject. Large number of posts raises the difficulties in information management of these sites. A number of approaches are suggested in recent research work for efficient management of data for CQA sites. Many of the existing approaches have suggested use of clustering techniques for managing the CQA sites, but ignored the tagging data (user tags) of the posts. In this paper, an improved spectral clustering technique is derived based on similarity measures for text processing (SMTP) and utilized for clustering the posts considering the tagging data available on CQA sites. A specialized data structure, namely, folksonomy is developed for clustering using the relationship between tags, posts and users.