Clustering algorithm recommendation: A meta-learning approach
dc.contributor.author | Ferrari D.G. | |
dc.contributor.author | De Castro L.N. | |
dc.date.accessioned | 2024-03-13T01:06:28Z | |
dc.date.available | 2024-03-13T01:06:28Z | |
dc.date.issued | 2012 | |
dc.description.abstract | Meta-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.firstpage | 143 | |
dc.description.lastpage | 150 | |
dc.description.volume | 7677 LNCS | |
dc.identifier.doi | 10.1007/978-3-642-35380-2_18 | |
dc.identifier.issn | 0302-9743 | |
dc.identifier.uri | https://dspace.mackenzie.br/handle/10899/36717 | |
dc.relation.ispartof | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | |
dc.rights | Acesso Restrito | |
dc.subject.otherlanguage | algorithm recommendation | |
dc.subject.otherlanguage | clustering | |
dc.subject.otherlanguage | meta-learning | |
dc.subject.otherlanguage | ranking | |
dc.title | Clustering algorithm recommendation: A meta-learning approach | |
dc.type | Artigo de evento | |
local.scopus.citations | 12 | |
local.scopus.eid | 2-s2.0-84871598548 | |
local.scopus.subject | clustering | |
local.scopus.subject | Clustering problems | |
local.scopus.subject | Data sets | |
local.scopus.subject | Meta-learning approach | |
local.scopus.subject | Meta-learning system | |
local.scopus.subject | Metalearning | |
local.scopus.subject | ranking | |
local.scopus.updated | 2024-05-01 | |
local.scopus.url | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84871598548&origin=inward |