Particle swarm optimization of information-content weighting of symbolic aggregate approximation

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

6 Citations (Scopus)


Bio-inspired optimization algorithms have been gaining more popularity recently. One of the most important of these algorithms is particle swarm optimization (PSO). PSO is based on the collective intelligence of a swam of particles. Each particle explores a part of the search space looking for the optimal position and adjusts its position according to two factors; the first is its own experience and the second is the collective experience of the whole swarm. PSO has been successfully used to solve many optimization problems. In this work we use PSO to improve the performance of a well-known representation method of time series data which is the symbolic aggregate approximation (SAX). As with other time series representation methods, SAX results in loss of information when applied to represent time series. In this paper we use PSO to propose a new minimum distance WMD for SAX to remedy this problem. Unlike the original minimum distance, the new distance sets different weights to different segments of the time series according to their information content. This weighted minimum distance enhances the performance of SAX as we show through experiments using different time series datasets.

Original languageEnglish
Title of host publicationAdvanced Data Mining and Applications - 8th International Conference, ADMA 2012, Proceedings
Number of pages13
ISBN (Electronic)978-3-642-35527-1
ISBN (Print)978-3-642-35526-4
Publication statusPublished - 1 Dec 2012
Externally publishedYes
Event8th International Conference on Advanced Data Mining and Applications - Nanjing, China
Duration: 15 Dec 201218 Dec 2012
Conference number: 8th

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7713 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference8th International Conference on Advanced Data Mining and Applications
Abbreviated titleADMA 2012


  • Bio-inspired optimization
  • Information content
  • Information loss
  • Particle swarm optimization
  • Symbolic aggregate approximation
  • Time series data mining

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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