Multiple data structure discovery through global optimisation, meta clustering and consensus methods

Authors:
Ida Bifulco;Carmine Fedullo;Francesco Napolitano;Giancarlo Raiconi;Roberto Tagliaferri
Affiliations:
NeuRoNe Lab, DMI, University of Salerno, via Ponte don Melillo, 84084 Fisciano, (SA) Italy.;NeuRoNe Lab, DMI, University of Salerno, via Ponte don Melillo, 84084 Fisciano, (SA) Italy.;NeuRoNe Lab, DMI, University of Salerno, via Ponte don Melillo, 84084 Fisciano, (SA) Italy.;NeuRoNe Lab, DMI, University of Salerno, via Ponte don Melillo, 84084 Fisciano, (SA) Italy.;NeuRoNe Lab, DMI, University of Salerno, via Ponte don Melillo, 84084 Fisciano, (SA) Italy
Venue:
International Journal of Knowledge Engineering and Soft Data Paradigms
Year:
2009

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Abstract

When dealing with real data, clustering becomes a very complex problem, usually admitting many reasonable solutions. Moreover, even if completely different, such solutions can appear almost equivalent from the point of view of classical quality measures such as the distortion value. This implies that blind optimisation techniques alone are prone to discard qualitatively interesting solutions. In this work we propose a systematic approach to clustering, including the generation of a number of good solutions through global optimisation, the analysis of such solutions through meta clustering and the final construction of a small set of solutions through consensus clustering.