Extending the edit distance using frequencies of common characters

Abstract

Similarity search of time series has attracted many researchers recently. In this scope, reducing the dimensionality of data is 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 effectiveness of similarity search we propose in this paper an extension to the edit distance that we call the extended edit distance. This new distance is applied to symbolic sequential data objects, and we test it on time series data bases in classification task experiments. We also prove that our distance is a metric. © 2008 Springer-Verlag Berlin Heidelberg.