Parameter-free extended edit distance

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


The edit distance is the most famous distance to compute the similarity between two strings of characters. The main drawback of the edit distance is that it is based on local procedures which reflect only a local view of similarity. To remedy this problem we presented in a previous work the extended edit distance, which adds a global view of similarity between two strings. However, the extended edit distance includes a parameter whose computation requires a long training time. In this paper we present a new extension of the edit distance which is parameter-free. We compare the performance of the new extension to that of the extended edit distance and we show how they both perform very similarly.

Original languageEnglish
Title of host publicationData Warehousing and Knowledge Discovery - 16th International Conference, DaWaK 2014, Proceedings
PublisherSpringer-Verlag Italia
Number of pages11
ISBN (Electronic)978-3-319-10160-6
ISBN (Print)978-3-319-10159-0
Publication statusPublished - 1 Jan 2014
Externally publishedYes
Event16th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2014 - Munich, Germany
Duration: 2 Sept 20144 Sept 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8646 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2014


  • Edit Distance
  • Extended Edit Distance
  • Parameter-Free Extended Edit Distance

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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