Towards normalizing the edit distance using a genetic algorithms-based scheme

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6 Citations (Scopus)

Abstract

The normalized edit distance is one of the distances derived from the edit distance. It is useful in some applications because it takes into account the lengths of the two strings compared. The normalized edit distance is not defined in terms of edit operations but rather in terms of the edit path. In this paper we propose a new derivative of the edit distance that also takes into consideration the lengths of the two strings, but the new distance is related directly to the edit distance. The particularity of the new distance is that it uses the genetic algorithms to set the values of the parameters it uses. We conduct experiments to test the new distance and we obtain promising results.

Original languageEnglish
Title of host publicationAdvanced Data Mining and Applications - 8th International Conference, ADMA 2012, Proceedings
PublisherSpringer
Pages477-487
Number of pages11
ISBN (Print)9783642355264
DOIs
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

Conference

Conference8th International Conference on Advanced Data Mining and Applications
Abbreviated titleADMA 2012
CountryChina
CityNanjing
Period15/12/1218/12/12

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Keywords

  • Edit distance
  • Genetic algorithms
  • Normalized edit distance
  • Sequential data

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Muhammad Fuad, M. M. (2012). Towards normalizing the edit distance using a genetic algorithms-based scheme. In Advanced Data Mining and Applications - 8th International Conference, ADMA 2012, Proceedings (pp. 477-487). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7713 LNAI). Springer. https://doi.org/10.1007/978-3-642-35527-1_40