Rapid identification of repeated patterns in strings, trees and arrays

Karp, Richard M.; Miller, Raymond E.; Rosenberg, Arnold L.

doi:10.1145/800152.804905

Cited by 215 publications

(117 citation statements)

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“…Using a statistic of extremes developed by Karlin and Ost [13] and an efficient algorithm to search for repeats [15,16], we have proceeded to an extensive analysis of repeats in eight prokaryotic genomes [14]. This was done after excluding simple repeated motifs and rRNA operons (often present in multiple copies).…”

Section: Distribution Of Long Strict Repeats In Bacterial Genomesmentioning

confidence: 99%

Functional and evolutionary roles of long repeats in prokaryotes

Rocha

Danchin

Viari³

1999

Research in Microbiology

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Section: Distribution Of Long Strict Repeats In Bacterial Genomesmentioning

confidence: 99%

Functional and evolutionary roles of long repeats in prokaryotes

Rocha

Danchin

Viari³

1999

Research in Microbiology

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“…Instead of storing in the dictionary the actual strings of P, we will assign a unique name for every string in P using the naming technique of Karp et al [13] (see also [1]), and we will store only these names.…”

Section: Building the Dictionarymentioning

confidence: 99%

Efficient One Dimensional Real Scaled Matching

Amir

Butman

Lewenstein

et al. 2004

String Processing and Information Retrieval

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Real Scaled Matching is the problem of finding all locations in the text where the pattern, proportionally enlarged according to an arbitrary real-sized scale, appears. Real scaled matching is an important problem that was originally inspired by Computer Vision.In this paper, we present a new, more precise and realistic, definition for one dimensional real scaled matching, and an efficient algorithm for solving this problem. For a text of length n and a pattern of length m, the algorithm runs in time O(n log m + √ nm 3/2 √ log m).

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“…However, the way in which we can build such clusters may differ. The classical approach is to detect highly connected sub-graphs of the similarity graph [4], [5]. However, for many reasons, this approach appears to be inappropriate to solve our problem.…”

Section: Pattern Extractionmentioning

confidence: 99%

“…In the past, an important amount of good results have been obtained on the exact matching problems between strings, areas or binary trees [4], [5]. Other approaches have dealt with approximate pattern matching.…”

Section: Introductionmentioning

confidence: 99%

Extraction of Recurrent Patterns from Stratified Ordered Trees

Ganascia¹

2001

Machine Learning: ECML 2001

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Abstract. This paper proposes a new algorithm for pattern extraction from Stratified Ordered Trees (SOT). It first describes the SOT data structure that renders possible a representation of structured sequential data. Then it shows how it is possible to extract clusters of similar recurrent patterns from any SOT. The similarity on which our clustering algorithm is based is a generalized edit distance, also described in the paper. The algorithms presented have been tested on text mining: the aim was to detect recurrent syntactical motives in texts drawn from classical literature. Hopefully, this algorithm can be applied to many different fields where data are naturally sequential (e.g. financial data, molecular biology, traces of computation, etc.)

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Rapid identification of repeated patterns in strings, trees and arrays

Cited by 215 publications

References 1 publication

Functional and evolutionary roles of long repeats in prokaryotes

Functional and evolutionary roles of long repeats in prokaryotes

Efficient One Dimensional Real Scaled Matching

Extraction of Recurrent Patterns from Stratified Ordered Trees

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