Clustering in Aggregated User Profiles Across Multiple Social Networks

A social network is indeed an abstraction of related groups interacting amongst themselves to develop relationships. However, toanalyze any relationships and psychology behind it, clustering plays a vital role. Clustering enhances the predictability and discoveryof like mindedness amongst users. This article’s goal exploits the technique of Ensemble K-means clusters to extract the entities and their corresponding interestsas per the skills and location by aggregating user profiles across the multiple online social networks. The proposed ensemble clustering utilizes known K-means algorithm to improve results for the aggregated user profiles across multiple social networks. The approach produces an ensemble similarity measure and provides 70% better results than taking a fixed value of K or guessing a value of K while not altering the clustering method. This paper states that good ensembles clusters can be spawned to envisage the discoverability of a user for a particular interest