Beyond classical consensus clustering: the Least Squares approach to multiple solutions (Articolo in rivista)

Type
Label
  • Beyond classical consensus clustering: the Least Squares approach to multiple solutions (Articolo in rivista) (literal)
Anno
  • 2011-01-01T00:00:00+01:00 (literal)
Alternative label
  • Murino L., Angelini C., De Feis I., Raiconi G., Tagliaferri R. (2011)
    Beyond classical consensus clustering: the Least Squares approach to multiple solutions
    in Pattern recognition letters
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Murino L., Angelini C., De Feis I., Raiconi G., Tagliaferri R. (literal)
Pagina inizio
  • 1604 (literal)
Pagina fine
  • 1612 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 32 (literal)
Rivista
Note
  • Scopu (literal)
  • ISI Web of Science (WOS) (literal)
  • Google Scholar (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • L. Murino a,b,?, C. Angelini b, I. De Feis b, G. Raiconi a, R. Tagliaferri a a NeuRoNe Lab, DMI University of Salerno, via Ponte don Melillo, 84084 Fisciano, SA, Italy b Istituto per le Applicazioni del Calcolo 'Mauro Picone' CNR, via Pietro Castellino, 111, 80131 Napoli, Italy (literal)
Titolo
  • Beyond classical consensus clustering: the Least Squares approach to multiple solutions (literal)
Abstract
  • Clustering is one of the most important unsupervised learning problems and it consists of finding a common structure in a collection of unlabeled data. However, due to the ill-posed nature of the problem, different runs of the same clustering algorithm applied to the same data-set usually produce different solutions. In this scenario choosing a single solution is quite arbitrary. On the other hand, in many applications the problem of multiple solutions becomes intractable, hence it is often more desirable to provide a limited group of ''good'' clusterings rather than a single solution. In the present paper we propose the least squares consensus clustering. This technique allows to extrapolate a small number of different clustering solutions from an initial (large) ensemble obtained by applying any clustering algorithm to a given data-set. We also define a measure of quality and present a graphical visualization of each consensus clustering to make immediately interpretable the strength of the consensus. We have carried out several numerical experiments both on synthetic and real data-sets to illustrate the proposed methodology. (literal)
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