Enhancing the symbolic aggregate approximation method using updated lookup tables

Muhammad Marwan Muhammad Fuad, Pierre François Marteau

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

5 Citations (Scopus)

Abstract

Similarity search in time series data mining is a problem that has attracted increasing attention recently. The high dimensionality and large volume of time series databases make sequential scanning inefficient to tackle this problem. There are many representation techniques that aim at reducing the dimensionality of time series so that the search can be handled faster at a lower dimensional space level. Symbolic representation is one of the promising techniques, since symbolic representation methods try to benefit from the wealth of search algorithms used in bioinformatics and text mining communities. The symbolic aggregate approximation (SAX) is one of the most competitive methods in the literature. SAX utilizes a similarity measure that is easy to compute because it is based on pre-computed distances obtained from lookup tables. In this paper we present a new similarity measure that is almost as easy to compute as the original similarity measure, but it is tighter because it uses updated lookup tables. In addition, the new similarity measure is more intuitive than the original one. We conduct several experiments which show that the new similarity measure gives better results than the original one.

Original languageEnglish
Title of host publicationKnowledge-Based and Intelligent Information and Engineering Systems - 14th International Conference, KES 2010, Proceedings
PublisherSpringer
Pages420-431
Number of pages12
EditionPART 1
ISBN (Electronic)978-3-642-15387-7
ISBN (Print)978-3-642-15386-0
DOIs
Publication statusPublished - 23 Nov 2010
Event14th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2010 - Cardiff, United Kingdom
Duration: 8 Sep 201010 Sep 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6276 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2010
CountryUnited Kingdom
CityCardiff
Period8/09/1010/09/10

Keywords

  • Symbolic Aggregate Approximation
  • Symbolic Representation
  • Time Series Data Mining
  • Updated Minimum Distance

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Enhancing the symbolic aggregate approximation method using updated lookup tables'. Together they form a unique fingerprint.

  • Cite this

    Muhammad Fuad, M. M., & Marteau, P. F. (2010). Enhancing the symbolic aggregate approximation method using updated lookup tables. In Knowledge-Based and Intelligent Information and Engineering Systems - 14th International Conference, KES 2010, Proceedings (PART 1 ed., pp. 420-431). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6276 LNAI, No. PART 1). Springer. https://doi.org/10.1007/978-3-642-15387-7_46