Approximate string matching

About: Approximate string matching is a research topic. Over the lifetime, 1903 publications have been published within this topic receiving 62352 citations. The topic is also known as: fuzzy string-searching algorithm & fuzzy string-matching algorithm.

System and method for improved string matching under noisy channel conditions

Abstract: Described is a system and method for improving string matching in a noisy channel environment. The invention provides a method for identifying string candidates and analyzing the probability that the string candidate matches a user-defined string. In one implementation, a find engine receives a query string, converts an image file into a textual file, and identifies each instance of the query string in the textual file. The find engine identifies candidates within the textual file that may match the query string. The find engine refers to a confusion table to help identify whether candidates that are near matches to the query string are actually matches to the query string but for a common recognition error. Candidates meeting a probability threshold are identified as matches to the query string. The invention further provides for analysis options including word heuristics, language models, and OCR confidences.

A comparison of three string matching algorithms

Abstract: Three string matching algorithms—straightforward, Knuth-Morris-Pratt and Boyer-Moor—re examined and their time complexities discussed. A comparison of their actual average behaviour is made, based on empirical data presented. It is shown that the Boyel-Moore algorithm is extremely efficient in most cases and that, contrary to the impression one might get from the analytical results, the Knuth-Morris-Pratt algorithm is not significantly better on the average than the straightforward algorithm.

Improving an Algorithm for Approximate Pattern Matching

Abstract: We study a recent algorithm for fast on-line approximate string matching. This is the problem of searching a pattern in a text allowing errors in the pattern or in the text. The algorithm is based on a very fast kernel which is able to search short patterns using a nondeterministic finite automaton, which is simulated using bit-parallelism. A number of techniques to extend this kernel for longer patterns are presented in that work. However, the techniques can be integrated in many ways and the optimal interplay among them is by no means obvious. The solution to this problem starts at a very low level, by obtaining basic probabilistic information about the problem which was not previously known, and ends integrating analytical results with empirical data to obtain the optimal heuristic. The conclusions obtained via analysis are experimentally confirmed. We also improve many of the techniques and obtain a combined heuristic which is faster than the original work. This work shows an excellent example of a complex and theoretical analysis of algorithms used for design and for practical algorithm engineering, instead of the common practice of first designing an algorithm and then analyzing it.

Optimization techniques for string selection and comparison problems in genomics

Abstract: In this article, a discussion of optimization issues occurring in the area of genomics such as string comparison and selection problems are discussed. With this objective, an important part of the existing results in this area will be discussed. The problems that are of interest in this paper include the closest string problem (CSP), closest substring problem (CSSP), farthest string problem (FSP), farthest substring problem (FSSP), and far from most string (FFMSP) problem. The paper presents a detailed view of the most important problems occurring in the area of string comparison and selection, using the Hamming distance measure is given.