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A New Unsupervised Learning for Clustering Using Geometric Associative Memories

  • Benjamín Cruz
  • Ricardo Barrón
  • Humberto Sossa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5856)

Abstract

Associative memories (AMs) have been extensively used during the last 40 years for pattern classification and pattern restoration. A new type of AMs have been developed recently, the so-called Geometric Associative Memories (GAMs), these make use of Conformal Geometric Algebra (CGA) operators and operations for their working. GAM’s, at the beginning, were developed for supervised classification, getting good results. In this work an algorithm for unsupervised learning with GAMs will be introduced. This new idea is a variation of the k-means algorithm that takes into account the patterns of the a specific cluster and the patterns of another clusters to generate a separation surface. Numerical examples are presented to show the functioning of the new algorithm.

Keywords

Associative Memory Unsupervised Learn Geometric Algebra Separation Surface Conformal Geometric Algebra 
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 2009

Authors and Affiliations

  • Benjamín Cruz
    • 1
  • Ricardo Barrón
    • 1
  • Humberto Sossa
    • 1
  1. 1.Center for Computing ResearchNational Polytechnic InstituteMéxico CityMéxico

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