The extended edit distance metric

The problem of similarity search has attracted increasing attention recently, because it has many applications. Time series are high dimensional data objects. In order to utilize an indexing structure that can effectively handle large time series databases, we need to reduce the dimensionality of these data objects. One of the promising techniques of dimensionality reduction is symbolic representation, which allows researchers to avail from the wealth of text-retrieval algorithms and techniques. To improve the effectiveness of similarity search we propose an extension to the well-known edit distance that we call the extended edit distance. This new distance is applied to symbolic sequential data objects. We test the proposed distance on time series data bases in classification task experiments. We also compare it to other distances that are well known in the literature for symbolic data objects, and we also prove, mathematically, that our new distance is metric.