The multi-resolution extended edit distance
Résumé
Similarity search is a fundamental problem in information technology. The main difficulty of this problem is the high dimensionality of the data objects. In large time series databases, it's important to reduce the dimensionality of these data objects, so that we can manage them. Symbolic representation is a promising technique of dimensionality reduction. In this paper we propose a new distance metric, which is applied to symbolic sequential data objects, and we test it on time series databases in classification task experiments. We also compare it to other distances that are well known in the literature for symbolic data objects, and we prove that it's metric.