Clustering algorithm recommendation: a meta-learning approach

Authors:
Daniel G. Ferrari;Leandro Nunes de Castro
Affiliations:
Natural Computing Laboratory (LCoN), Mackenzie Presbyterian University, São Paulo, Brazil;Natural Computing Laboratory (LCoN), Mackenzie Presbyterian University, São Paulo, Brazil
Venue:
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
Year:
2012

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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.