Multiple Clustering Solutions Analysis Through Lest-Square Consensus Algorithms (Articolo in rivista)

Type
Label
  • Multiple Clustering Solutions Analysis Through Lest-Square Consensus Algorithms (Articolo in rivista) (literal)
Anno
  • 2010-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1007/978-3-642-14571-1_16 (literal)
Alternative label
  • Murino L., Angelini C., Bifulco I., De Feis I., Raiconi G., Tagliaferri R. (2010)
    Multiple Clustering Solutions Analysis Through Lest-Square Consensus Algorithms
    in Lecture notes in computer science; Springer-Verlag Berlin, Berlin (Germania)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Murino L., Angelini C., Bifulco I., De Feis I., Raiconi G., Tagliaferri R. (literal)
Pagina inizio
  • 215 (literal)
Pagina fine
  • 227 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 6160 (literal)
Rivista
Note
  • Google Scholar (literal)
  • ISI Web of Science (WOS) (literal)
  • Scopus (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Angelini, De Feis: IAC - CNR Murino, Bifulco, Raiconi, Tagliaferri: Universita' di Salerno (literal)
Titolo
  • Multiple Clustering Solutions Analysis Through Lest-Square Consensus Algorithms (literal)
Abstract
  • Clustering is one of the most important unsupervised learning problems and it deals with finding a structure in a collection of unlabeled data; however, different clustering algorithms applied to the same data-set produce different solutions. In many applications the problem of multiple solutions becomes crucial and providing a limited group of good clusterings is often more desirable than a single solution. In this work we propose the Least Square Consensus clustering that allows a user to extrapolate a small number of different clustering solutions from an initial (large) set of solutions obtained by applying any clustering algorithm to a given data-set. Two different implementations are presented. In both cases, each consensus is accomplished with a measure of quality defined in terms of Least Square error and a graphical visualization is provided in order to make immediately interpretable the result. Numerical experiments are carried out on both synthetic and real data-sets. (literal)
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