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Emerging Paradigms in Machine Learning pp 31–48Cite as

Optimised Information Abstraction in Granular Min/Max Clustering

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Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 13))

Abstract

The Min/Max classification and clustering has a distinct advantage of generating easily interpretable information granules - represented as hyperboxes in the multi-dimensional feature space of the data. However, while such an information abstraction lends itself to easy interpretation it leaves open the question whether the granules represent well the original data.

In this chapter we discuss an approach to optimised information abstraction, which retains the advantages of Min/Max clustering while providing a basis for building a more representative set of granules. In particular we extend the information density based granulation by including an extra stage of optimised refinement of granular prototypes. The initial granulation is accomplished by creating hyperboxes in the pattern space through the maximisation of the count of data items per unit volume of hyperboxes. The granulation is totally data driven in that it does not make any assumptions about the number or the maximum size of hyperboxes. Subsequent optimisation involves identification of granular prototypes and their refinement so as to achieve full reconstruction of the original data from the prototypes and the corresponding partition matrix.

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Author information

Authors and Affiliations

  1. University of Nottingham, Nottingham, UK

    Andrzej Bargiela

  2. University of Alberta, Edmonton, Canada

    Witold Pedrycz

Authors
  1. Andrzej Bargiela
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  2. Witold Pedrycz
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Corresponding author

Correspondence to Andrzej Bargiela .

Editor information

Editors and Affiliations

  1. Deptartment of Applied Computer Science, University of Winnipeg, 515 Portage Avenue, Winnipegg, R3B 2E9, Canada

    Sheela Ramanna

  2. , School of Electrical and Information, University of South Australia, Adelaide, SA 5095, Australia

    Lakhmi C Jain

  3. KES International, Shoreham-by-sea, BN43 9AF, United Kingdom

    Robert J. Howlett

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Bargiela, A., Pedrycz, W. (2013). Optimised Information Abstraction in Granular Min/Max Clustering. In: Ramanna, S., Jain, L., Howlett, R. (eds) Emerging Paradigms in Machine Learning. Smart Innovation, Systems and Technologies, vol 13. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28699-5_3

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  • DOI: https://doi.org/10.1007/978-3-642-28699-5_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28698-8

  • Online ISBN: 978-3-642-28699-5

  • eBook Packages: EngineeringEngineering (R0)

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