SOM training optimization using triangle inequality

Denny, William Gozali, Ruli Manurung

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Triangle inequality optimization is one of several strategies on the k- means algorithm that can reduce the search space in finding the nearest prototype vector. This optimization can also be applied towards Self-Organizing Maps training, particularly during finding the best matching unit in the batch training approach. This paper investigates various implementations of this optimization and measures the efficiency gained on various datasets, dimensions, maps, cluster size and density. Our experiments on synthetic and real life datasets show that the number of comparisons can be reduced to 24% and the running time can also reduced to between 63 and 87%.

Original languageEnglish
Title of host publicationAdvances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 11th International Workshop WSOM 2016
EditorsPatrick O’Driscoll, Michael J. Mendenhall, Erzsébet Merényi
PublisherSpringer Verlag
Pages61-71
Number of pages11
ISBN (Print)9783319285177
DOIs
Publication statusPublished - 1 Jan 2016
Event11th International on Advances in Self-Organizing Maps and Learning Vector Quantization Workshop, WSOM 2016 - Houston, United States
Duration: 6 Jan 20168 Jan 2016

Publication series

NameAdvances in Intelligent Systems and Computing
Volume428
ISSN (Print)2194-5357

Conference

Conference11th International on Advances in Self-Organizing Maps and Learning Vector Quantization Workshop, WSOM 2016
CountryUnited States
CityHouston
Period6/01/168/01/16

Keywords

  • Implementation
  • Optimization
  • Self-Organizing Map
  • Triangle inequality

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