Clustering algorithm recommendation: A meta-learning approach

Ferrari D.G.; De Castro L.N.

Clustering algorithm recommendation: A meta-learning approach

Tipo

Artigo de evento

Data de publicação

2012

Periódico

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Citações (Scopus)

12

Autores

Ferrari D.G.
De Castro L.N.

Resumo

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.

Assuntos Scopus

clustering , Clustering problems , Data sets , Meta-learning approach , Meta-learning system , Metalearning , ranking