Social web videos clustering based on ensemble technique

Abstract

Currently, a massive amount of videos has become a challenging research area for social web videos mining. Clustering ensemble is a common approach to clustering problems, which combine a collection of clusterings into a superior solution. Textual features are widely used to describe a web video. Whereas, local and global features also have their own advantages to describe a web video as well. So we extract the local and global features as we called low-level/semantic features and high-level/visual features respectively to help to better describe a main source. In this paper, we propose a combining function of three similarity models to enhance the similarity values of videos, and then present a framework for Clustering Ensemble with the support of Must-Link constraint (CE-ML) to formulate in ensembling for clustering purposes. Experimental evaluation on the real world social web video has been performed to validate the proposed framework.