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Clustering Algorithm Recommendation: A Meta-learning Approach

  • Daniel G. Ferrari
  • Leandro Nunes de Castro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7677)

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.

Keywords

clustering algorithm recommendation ranking meta-learning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Daniel G. Ferrari
    • 1
  • Leandro Nunes de Castro
    • 1
  1. 1.Natural Computing Laboratory (LCoN)Mackenzie Presbyterian UniversitySão PauloBrazil

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