Ensemble Clustering for Result Diversification

Abstract

This paper describes the participation of the University ofTwente in the Web track of TREC 2012. Our baseline approachuses the Mirex toolkit, an open source tool that sequantiallyscans all the documents. For result diversification,we experimented with improving the quality of clustersthrough ensemble clustering. We combined clusters obtainedby different clustering methods (such as LDA andK-means) and clusters obtained by using different types ofdata (such as document text and anchor text). Our twolayerensemble run performed better than the LDA based diversificationand also better than a non-diversification run