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DAEDALUS at WebPS-3 2010: k-Medoids Clustering Using a Cost Function Minimization

This paper describes the participation of DAEDALUS team at the WebPS-3 Task 1, regarding Web People Search. The focus of our research is to evaluate and compare the computational requirements and results achieved by different solutions based on the minimization of cost functions applied to clustering algorithms. Our clustering technique is based on an implementation of k-Medoids algorithm, run over a sparse term-document matrix built with the terms of the pages that are associated to each of the person names. We define an empty-cluster that holds all the individuals that are not part of any other cluster. Based on the results obtained, we can conclude that although clustering techniques play a very relevant role in the resolution of the problem of name homonymy in a set of web pages, there is a previous challenge still to solve: how to determine which contents are relevant for describing the person in that webpage, thus which are not part of the other navigational information contained in the webpage.