Extending the Edit Distance Using Frequencies of Common Characters
M. Fuad, and P. Marteau. Database and Expert Systems Applications, (2008)
Abstract
Similarity search of time series has attracted many researchers recently. In this scope, reducing the dimensionality of datais required to scale up the similarity search. Symbolic representation is a promising technique of dimensionality reduction,since it allows researchers to benefit from the richness of algorithms used for textual databases. To improve the effectivenessof similarity search we propose in this paper an extension to the edit distance that we call the extended edit distance. Thisnew distance is applied to symbolic sequential data objects, and we test it on time series data bases in classification taskexperiments. We also prove that our distance is a metric.
%0 Journal Article
%1 fuad08distance
%A Fuad, Muhammad Muhammad
%A Marteau, Pierre-François
%D 2008
%J Database and Expert Systems Applications
%K research.metrics research.clustering.similarity
%P 150--157
%T Extending the Edit Distance Using Frequencies of Common Characters
%U http://dx.doi.org/10.1007/978-3-540-85654-2_18
%X Similarity search of time series has attracted many researchers recently. In this scope, reducing the dimensionality of datais required to scale up the similarity search. Symbolic representation is a promising technique of dimensionality reduction,since it allows researchers to benefit from the richness of algorithms used for textual databases. To improve the effectivenessof similarity search we propose in this paper an extension to the edit distance that we call the extended edit distance. Thisnew distance is applied to symbolic sequential data objects, and we test it on time series data bases in classification taskexperiments. We also prove that our distance is a metric.
@article{fuad08distance,
abstract = {Similarity search of time series has attracted many researchers recently. In this scope, reducing the dimensionality of datais required to scale up the similarity search. Symbolic representation is a promising technique of dimensionality reduction,since it allows researchers to benefit from the richness of algorithms used for textual databases. To improve the effectivenessof similarity search we propose in this paper an extension to the edit distance that we call the extended edit distance. Thisnew distance is applied to symbolic sequential data objects, and we test it on time series data bases in classification taskexperiments. We also prove that our distance is a metric.},
added-at = {2010-10-07T11:10:48.000+0200},
author = {Fuad, Muhammad Muhammad and Marteau, Pierre-François},
biburl = {https://www.bibsonomy.org/bibtex/27e1384aa40484cc872e096fd8f952913/msn},
description = {SpringerLink - Book Chapter},
interhash = {b64bac6378f95af75e33494f8f45165a},
intrahash = {7e1384aa40484cc872e096fd8f952913},
journal = {Database and Expert Systems Applications},
keywords = {research.metrics research.clustering.similarity},
pages = {150--157},
timestamp = {2010-10-07T11:10:48.000+0200},
title = {Extending the Edit Distance Using Frequencies of Common Characters},
url = {http://dx.doi.org/10.1007/978-3-540-85654-2_18},
year = 2008
}