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Optimised Information Abstraction in Granular Min/Max Clustering

  • Andrzej Bargiela
  • Witold Pedrycz
Part of the Smart Innovation, Systems and Technologies book series (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.

Keywords

Particle Swarm Optimisation Data Item Pattern Space Information Granule Granular Computing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.University of NottinghamNottinghamUK
  2. 2.University of AlbertaEdmontonCanada

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