Multiple clustering solutions analysis through least-squares consensus algorithms (Articolo in rivista)

Type
Label
  • Multiple clustering solutions analysis through least-squares 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.a and Angelini, C.b and Bifulco, I.a and De Feis, I.b and Raiconi, G.a and Tagliaferri, R.a (2010)
    Multiple clustering solutions analysis through least-squares consensus algorithms
    in Lecture notes in computer science
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Murino, L.a and Angelini, C.b and Bifulco, I.a and De Feis, I.b and Raiconi, G.a and Tagliaferri, R.a (literal)
Pagina inizio
  • 215 (literal)
Pagina fine
  • 227 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • cited By (since 1996)0; Conference of org.apache.xalan.xsltc.dom.DOMAdapter@4aa9913b ; Conference Date: org.apache.xalan.xsltc.dom.DOMAdapter@427ddec8 Through org.apache.xalan.xsltc.dom.DOMAdapter@55ad3e92; Conference Code:81435 (literal)
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  • http://www.scopus.com/inward/record.url?eid=2-s2.0-77955814233&partnerID=40&md5=084097704038c55d7ecf477b79120609 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 6160 LNBI (literal)
Rivista
Note
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • NeuRoNe Lab., DMI, University of Salerno, via Ponte don Melillo, 84084 Fisciano, (SA), Italy; Istituto Per Le Applicazioni del Calcolo 'Mauro Picone', CNR, via Pietro Castellino, 111, 80131 Napoli, Italy (literal)
Titolo
  • Multiple clustering solutions analysis through least-squares 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. © 2010 Springer-Verlag. (literal)
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