A Bit-Vector Algorithm for Computing Levenshtein and Damerau Edit Distances

A Bit-Vector Algorithm for Computing Levenshtein and Damerau Edit Distances

Practical Methods for Approximate String Matching

Given a pattern string and a text, the task of approximate string matching is to find all locations in the text that are similar to the pattern. This type of search may be done for example in applications of spelling error correction or bioinformatics. Typically edit distance is used as the measure of similarity (or distance) between two strings. In this thesis we concentrate on unit-cost edit ...

A Note on Bit-Parallel Alignment Computation

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. Typically the allowed edit operations are one or more of the following: an insertion, a deletion or a substitution of a character, or a transposition between two adjacent characters. Simple edit distance allows the first two operation types, Levenshtein edit...

Bit-Parallel Approximate String Matching Algorithms with Transposition

Using bit-parallelism has resulted in fast and practical algorithms for approximate string matching under the Levenshtein edit distance, which permits a single edit operation to insert, delete or substitute a character. Depending on the parameters of the search, currently the fastest non-filtering algorithms in practice are the O(kn!m/w") algorithm of Wu & Manber, the O(!km/w"n) algorithm of Ba...

Fuzzy lexical matching

Being able to automatically correct spelling errors is useful in cases where the set of documents is too vast to involve human interaction. In this bachelor's thesis, we investigate an implementation that attempts to perform such corrections using a lexicon and edit distance measure. We compare the familiar Levenshtein and Damerau-Levenshtein distances to modi cations where each edit operation ...