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Clustering Rules: A Comparison of Partitioning and Hierarchical Clustering Algorithms

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Journal of Mathematical Modelling and Algorithms

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.

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Authors and Affiliations

  1. School of Computing Sciences, University of East Anglia, Norwich, UK

    A. P. Reynolds, G. Richards, B. de la Iglesia & V. J. Rayward-Smith

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  1. A. P. Reynolds
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  2. G. Richards
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  3. B. de la Iglesia
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  4. V. J. Rayward-Smith
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Correspondence to A. P. Reynolds.

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Reynolds, A.P., Richards, G., de la Iglesia, B. et al. Clustering Rules: A Comparison of Partitioning and Hierarchical Clustering Algorithms. J Math Model Algor 5, 475–504 (2006). https://doi.org/10.1007/s10852-005-9022-1

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  • DOI: https://doi.org/10.1007/s10852-005-9022-1

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