@inproceedings{559efbd01a5246d0928ee46077a212ae,
title = "Parameter-free extended edit distance",
abstract = "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.",
keywords = "Edit Distance, Extended Edit Distance, Parameter-Free Extended Edit Distance",
author = "{Muhammad Fuad}, {Muhammad Marwan}",
year = "2014",
month = jan,
day = "1",
doi = "10.1007/978-3-319-10160-6_41",
language = "English",
isbn = "978-3-319-10159-0",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag Italia",
pages = "465--475",
booktitle = "Data Warehousing and Knowledge Discovery - 16th International Conference, DaWaK 2014, Proceedings",
address = "Italy",
note = "16th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2014 ; Conference date: 02-09-2014 Through 04-09-2014",
}