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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.


Papers
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Journal ArticleDOI
06 Jan 1992
TL;DR: An approximate string-matching algorithm is described based on earlier attribute- matching algorithms, which involves building a trie from the text string which takes time O(N log2 N), for a text string of length N.
Abstract: An approximate string-matching algorithm is described based on earlier attribute-matching algorithms. The algorithm involves building a trie from the text string which takes time O(N log2 N), for a text string of length N. Once this data structure has been built any number of approximate searches can be made for pattern strings of length m. The expected complexity analysis is given for the look-up phase of the algorithm based on certain regularity assumptions about the background language. The expected look-up time for each pattern is O(m log2 N). The ideas employed in the algorithm have been shown effective in practice before, but have not previously received any theoretical analysis.

25 citations

Proceedings ArticleDOI
30 Oct 2008
TL;DR: This work presents an approach to measuring similarities between visual data based on approximate string matching, and shows that such a globally ordered and locally unordered representation is more discriminative than a bag-of-features representation and the similarity measure based on string matching is effective.
Abstract: We present an approach to measuring similarities between visual data based on approximate string matching. In this approach, an image is represented by an ordered list of feature descriptors. We show the extraction of local features sequences from two types of 2-D signals - scene and shape images. The similarity of these two images is then measured by 1) solving a correspondence problem between two ordered sets of features and 2) calculating similarities between matched features and dissimilarities between unmatched features. Our experimental study shows that such a globally ordered and locally unordered representation is more discriminative than a bag-of-features representation and the similarity measure based on string matching is effective. We illustrate the application of the proposed approach to scene classification and shape retrieval, and demonstrate superior performance to existing solutions.

25 citations

Journal ArticleDOI
TL;DR: Experimental results indicate that the hostage string matching approach significantly improves the recognition rates compared to the one-stage string matching method.
Abstract: A two-stage string matching method for the recognition of two-dimensional (2-D) objects is proposed in this work. The first stage is a global cyclic string matching. The second stage is a local matching with local dissimilarity measure computing. The dissimilarity measure function of the input shape and the reference shape are obtained by combining the global matching cost and the local dissimilarity measure. The proposed method has the advantage that there is no need to set any parameter in the recognition process. Experimental results indicate that the hostage string matching approach significantly improves the recognition rates compared to the one-stage string matching method.

25 citations

Journal ArticleDOI
Tatsuya Akutsu1
TL;DR: This paper presents an O(√kmnpolylog(m) time algorithm for approximate string matching (k-differences problem), in which don't care characters may appear both in a pattern string and in a text string.

25 citations

Proceedings ArticleDOI
26 Apr 2004
TL;DR: The prototype of an experimental system that moves processing closer to where the data resides, on the disk, and exploits massive parallelism via reconfigurable hardware to perform the computation.
Abstract: Summary form only given. Data mining is an application that is commonly executed on massively parallel systems, often using clusters with hundreds of processors. With a disk-based data store, however, the data must first be delivered to the processors before effective mining can take place. Here, we describe the prototype of an experimental system that moves processing closer to where the data resides, on the disk, and exploits massive parallelism via reconfigurable hardware to perform the computation. The performance of the prototype is also reported.

25 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20238
202230
202132
202030
201948
201839