A faster and more accurate heuristic for cyclic edit distance computation

Ayad, Lorraine A. K.; Barton, Carl; Pissis, Solon P.

doi:10.1016/j.patrec.2017.01.018

Cited by 8 publications

(6 citation statements)

References 26 publications

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“…j ′ ], rot y (P )) ≤ γ. We cover T with fragments of length ⌊ 3 2 |P 1 |⌋+k starting at multiples of ⌊ 1 2 |P 1 |⌋. (The last fragments can be shorter.)…”

Section: ▶ Examplementioning

confidence: 99%

“…In many real-world applications, such as in bioinformatics [4,22,25,7] or in image processing [3,33,34,32], any cyclic shift (rotation) of P is a relevant pattern, and thus one is interested in computing the minimal distance of every length-m substring of T and any cyclic shift of P , if this distance is no more than k. This is the circular pattern matching with k mismatches (k-CPM) problem. A multitude of papers [17,8,6,5,9,24] have thus been devoted to solving the k-CPM problem but, to the best of our knowledge, only average-case upper bounds are known; i.e.…”

Section: Introductionmentioning

confidence: 99%

“…In bioinformatics, we need to account for DNA sequence divergence (e.g., in the comparison of different species or individuals); and in image processing, we need to account for small differences in the comparison of images (e.g., in classifying handwritten digits). This gives rise to the notion of edit distance on circular strings [35,3].…”

Section: Introductionmentioning

confidence: 99%

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Circular Pattern Matching with k Mismatches

Charalampopoulos

Kociumaka

Pissis

et al. 2019

Fundamentals of Computation Theory

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The k-mismatch problem consists in computing the Hamming distance between a pattern P of length m and every length-m substring of a text T of length n, if this distance is no more than k. In many real-world applications, any cyclic shift of P is a relevant pattern, and thus one is interested in computing the minimal distance of every length-m substring of T and any cyclic shift of P . This is the circular pattern matching with k mismatches (k-CPM) problem. A multitude of papers have been devoted to solving this problem but, to the best of our knowledge, only average-case upper bounds are known. In this paper, we present the first non-trivial worst-case upper bounds for the k-CPM problem. Specifically, we show an O(nk)-time algorithm and an O(n + n m k 5 )-time algorithm. The latter algorithm applies in an extended way a technique that was very recently developed for the k-mismatch problem [Bringmann et al., SODA 2019].

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“…j ′ ], rot y (P )) ≤ γ. We cover T with fragments of length ⌊ 3 2 |P 1 |⌋+k starting at multiples of ⌊ 1 2 |P 1 |⌋. (The last fragments can be shorter.)…”

Section: ▶ Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Circular Pattern Matching with k Mismatches

Charalampopoulos

Kociumaka

Pissis

et al. 2019

Fundamentals of Computation Theory

Self Cite

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“…Rivault et al [15] proposed a generalization of the LCS to measure the events' semantic similarity. Ayad et al [16] extended the cyclic edit distance based on q-gram to improve the computational speed and accuracy. Zhang et al [17] embedded the edit distance with a real penalty into difference-weighted KNN classifiers to realize the classification of the pulse waveform.…”

Section: Introductionmentioning

confidence: 99%

A New Time Series Similarity Measurement Method Based on the Morphological Pattern and Symbolic Aggregate Approximation

et al. 2019

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Aiming at the problem that the traditional similarity measurement methods cannot effectively measure the similarity of the time series with the difference both in the trend and detail, this paper proposes a new time series similarity measurement method (MP-SAX) based on the morphological pattern (MP) and symbolic aggregate approximation (SAX). According to the empirical mode decomposition (EMD), the time series are decomposed and reconstructed into the trend component and the detail component. Then, the similarity of the trend component under morphological pattern coding and that of the detail component under symbolic aggregate approximation coding are respectively calculated by the longest common subsequence (LCS). Finally, the similarity of the time series is obtained by weighted aggregation of the similarity of trend component and detail component. The MP-SAX is verified by the simulation time series and the time series from UCR Time Series Classification / Clustering Homepage. The results show that the MP-SAX can effectively measure the similarity of the time series with the changes both in trend and detail.

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“…This algorithm is a weighting approach to appoint a cost of 1 to every edit operations (Insertion, deletion and substitution). This distance is known as Levenshtein distance, a special case of edit distance where unit costs apply [5]. By using this algorithm, the searching perform is more accurate.…”

Section: Introductionmentioning

confidence: 99%

Autocomplete and Spell Checking Levenshtein Distance Algorithm To Getting Text Suggest Error Data Searching In Library

Yulianto

Arifudin

Alamsyah

2018

SJI

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Nowadays internet technology provide more convenience for searching information on a daily. Users are allowed to find and publish their resources on the internet using search engine. Search engine is a computer program designed to facilitate a user to find the information or data that they need. Search engines generally find the data based on keywords they entered, therefore a lot of case when the user can’t find the data that they need because there are an error while entering a keyword. Thats why a search engine with the ability to detect the entered words is required so the error can be avoided while we search the data. The feature that used to provide the text suggestion is autocomplete and spell checking using Levenshtein distance algorithm. The purpose of this research is to apply the autocomplete feature and spell checking with Levenshtein distance algorithm to get text suggestion in an error data searching in library and determine the level of accuracy on data search trials. This research using 1155 data obtained from UNNES Library. The variables are the input process and the classification of books. The accuracy of Levenshtein algorithm is 86% based on 1055 source case and 100 target case.

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A faster and more accurate heuristic for cyclic edit distance computation

Cited by 8 publications

References 26 publications

Circular Pattern Matching with k Mismatches

Circular Pattern Matching with k Mismatches

A New Time Series Similarity Measurement Method Based on the Morphological Pattern and Symbolic Aggregate Approximation

Autocomplete and Spell Checking Levenshtein Distance Algorithm To Getting Text Suggest Error Data Searching In Library

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