Clustering algorithm recommendation: A meta-learning approach

Página do item simplificado
dc.contributor.authorFerrari D.G.
dc.contributor.authorDe Castro L.N.
dc.date.accessioned2024-03-13T01:06:28Z
dc.date.available2024-03-13T01:06:28Z
dc.date.issued2012
dc.description.abstractMeta-learning is a technique that aims at understanding what types of algorithms solve what kinds of problems. Clustering, by contrast, divides a dataset into groups based on the objects' similarities without the need of previous knowledge about the objects' labels. The present paper proposes the use of meta-learning to recommend clustering algorithms based on the feature extraction of unlabelled objects. The features of the clustering problems will be evaluated along with the ranking of different algorithms so that the meta-learning system can recommend accurately the best algorithms for a new problem. © 2012 Springer-Verlag.
dc.description.firstpage143
dc.description.lastpage150
dc.description.volume7677 LNCS
dc.identifier.doi10.1007/978-3-642-35380-2_18
dc.identifier.issn0302-9743
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/36717
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.rightsAcesso Restrito
dc.subject.otherlanguagealgorithm recommendation
dc.subject.otherlanguageclustering
dc.subject.otherlanguagemeta-learning
dc.subject.otherlanguageranking
dc.titleClustering algorithm recommendation: A meta-learning approach
dc.typeArtigo de evento
local.scopus.citations12
local.scopus.eid2-s2.0-84871598548
local.scopus.subjectclustering
local.scopus.subjectClustering problems
local.scopus.subjectData sets
local.scopus.subjectMeta-learning approach
local.scopus.subjectMeta-learning system
local.scopus.subjectMetalearning
local.scopus.subjectranking
local.scopus.updated2024-05-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84871598548&origin=inward
Arquivos
Coleções
Artigo de evento