Differential evolution-based weighted combination  of distance metrics for k-means clustering

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

5 Citations (Scopus)

Abstract

Bio-inspired optimization algorithms have been successfully used to solve many problems in engineering, science, and economics. In computer science bio-inspired optimization has different applications in different domains such as software engineering, networks, data mining, and many others. One of the main tasks in data mining is clustering, namely k-means clustering. Distance metrics are at the heart of all data mining tasks. In this paper we present a new method which applies differential evolution, one of the main bio-inspired optimization algorithms, on a time series k-means clustering task to set the weights of the distance metrics used in a combination that is used to cluster the time series. The weights are obtained by applying an optimization process that gives optimal clustering quality. We show through extensive experiments how this optimized combination outperforms all the other stand-alone distance metrics, all by keeping the same low complexity of the distance metrics used in the combination.

Original languageEnglish
Title of host publicationTheory and Practice of Natural Computing - 3rd International Conference, TPNC 2014, Proceedings
EditorsAdrian-Horia Dediu, Manuel Lozano, Carlos Martín-Vide
PublisherSpringer-Verlag Italia
Pages193-204
Number of pages12
ISBN (Electronic)9783319137483
DOIs
Publication statusPublished - 1 Jan 2014
Event3rd International Conference on the Theory and Practice of Natural Computing - Granada, Spain
Duration: 9 Dec 201411 Dec 2014
Conference number: 3rd

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8890
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Conference on the Theory and Practice of Natural Computing
Abbreviated titleTPNC 2014
CountrySpain
CityGranada
Period9/12/1411/12/14

Keywords

  • Differential evolution
  • Distance metrics
  • Evolutionary computing
  • k-means clustering
  • Time series data mining

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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