Edit distance is a fundamental measure of distance between strings, the extensive study of which has recently focused on computational problems such as nearest neighbor search, sketching and fast approximation. A very powerful paradigm is to map the metric space induced by the edit distance into a normed space (e. g., `1) with small distortion, and then use the rich algorithmic toolkit known fo...

Computing the Edit Distance between two strings is one of the most fundamental problems in computer science. Algorithms based on edit distance are used extensively in the alignment of biological sequences. Any improvement either in time or space in solving this problem will be highly desirable. The standard dynamic programming based algorithm to compute the edit distance of two strings S1 = [a1...

Tree structured data often appear in bioinformatics. For example, glycans, RNA secondary structures and phylogenetic trees usually have tree structures. Comparison of trees is one of fundamental tasks in analysis of these data. Various distance measures have been proposed and utilized for comparison of trees, among which extensive studies have been done on tree edit distance. In this paper, we ...

The edit distance between two graphs on the same labeled vertex set is the symmetric difference of the edge sets. The edit distance function of hereditary property, H, is a function of p ∈ [0, 1] and is the limit of the maximum normalized distance between a graph of density p and H. This paper uses localization, for computing the edit distance function of various hereditary properties. For any ...

The edit distance between strings A and B is defined as the minimum number of edit operations needed in converting A into B or vice versa. The Levenshtein edit distance allows three types of operations: an insertion, a deletion or a substitution of a character. The Damerau edit distance allows the previous three plus in addition a transposition between two adjacent characters. To our best knowl...

This paper introduces a new method to improve tree edit distance approach to textual entailment recognition, using particle swarm optimization. Currently, one of the main constraints of recognizing textual entailment using tree edit distance is to tune the cost of edit operations, which is a difficult and challenging task in dealing with the entailment problem and datasets. We tried to estimate...

Abstract Quasiperiodicity in strings was introduced almost 30 years ago as an extension of string periodicity. The basic notions quasiperiodicity are cover and seed. A a text T is whose occurrences all positions . seed superstring In various applications exact still not sufficient due to the presence errors. We consider approximate quasiperiodicity, for which we allow with small Hamming, Levens...

This paper is concerned with computing graph edit distance. One of the criticisms that can be leveled at existing methods for computing graph edit distance is that it lacks the formality and rigour of the computation of string edit distance. Hence, our aim is to convert graphs to string sequences so that standard string edit distance techniques can be used. To do this we use graph spectral seri...