Journal of Mathematical Modelling and Algorithms

, Volume 5, Issue 4, pp 475–504

Clustering Rules: A Comparison of Partitioning and Hierarchical Clustering Algorithms

  • A. P. Reynolds
  • G. Richards
  • B. de la Iglesia
  • V. J. Rayward-Smith
Article

DOI: 10.1007/s10852-005-9022-1

Cite this article as:
Reynolds, A.P., Richards, G., de la Iglesia, B. et al. J Math Model Algor (2006) 5: 475. doi:10.1007/s10852-005-9022-1

Abstract

Previous research has resulted in a number of different algorithms for rule discovery. Two approaches discussed here, the ‘all-rules’ algorithm and multi-objective metaheuristics, both result in the production of a large number of partial classification rules, or ‘nuggets’, for describing different subsets of the records in the class of interest. This paper describes the application of a number of different clustering algorithms to these rules, in order to identify similar rules and to better understand the data.

Kew words

clusteringpartial classificationrule induction

Mathematics Subject Classifications (2000)

62H3068T0568T37

Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  • A. P. Reynolds
    • 1
  • G. Richards
    • 1
  • B. de la Iglesia
    • 1
  • V. J. Rayward-Smith
    • 1
  1. 1.School of Computing SciencesUniversity of East AngliaNorwichUK