A review on parameterized string matching algorithms

Singh, R. K.; Rai, Dinesh; Prasad, Rajesh

doi:10.1080/02522667.2017.1374730

Cited by 2 publications

(3 citation statements)

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“…The work in [20] is based on the approach proposed in [2]; it introduces an order-preserving match, but it limits the number of mismatches to k. Some solutions to the parameterized string matching problem based on bijective matchings focus on efficiency. In particular, [8] surveys some approaches that exploit q-grams in order to achieve linear time complexity; this last feature is also obtained with the support of position heaps [21]. More recently, parameterized matching has been studied also in its compressed version [22], [23].…”

Section: Related Workmentioning

confidence: 99%

“…These approaches present some limitations on two orthogonal aspects, namely: (i) the function used to match symbols from different alphabets, and (ii) the metric used to compare strings. In particular, bijection is the most commonly adopted matching function [7], [8]. However, bijective functions allow only 1-1 matching and, in some cases, this may be not enough.…”

Section: Introductionmentioning

confidence: 99%

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A General Approach to Uniformly Handle Different String Metrics Based on Heterogeneous Alphabets

et al. 2020

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In the last few years, we have assisted in a great increase of the usage of strings in the most disparate areas. In the meantime, the development of the Internet has brought the necessity of managing strings from very different contexts and possibly using different alphabets. This issue is not addressed by the numerous string comparison metrics previously proposed in the literature. In this paper, we aim at providing a contribution in this context. In fact, first we propose an approach to measure the similarity of strings based on different alphabets. Then we show that our approach can be specifically adapted to several classic string comparison metrics and that each specialization can lead to addressing completely different issues.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A General Approach to Uniformly Handle Different String Metrics Based on Heterogeneous Alphabets

et al. 2020

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“…Pattern matching and retrieval of information in text consider the key principles of data mining such as classical string matching [19,20]. The string matching problem is a pervasive problem widely applied in numerous applications area such as image searching, computational biology, and plagiarism detection; generally using the two classes; exact and approximate string matching [21,22,23].…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Text Mining Approach using Dynamic Programming

2018

IJISCS

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Text mining is a pervasive area of research in Computer Science. This is the process of finding knowledgeable and useful information and patterns from very huge text. It is widely applied in several areas of applications such as information retrievals; computational biology etc. Keyword-based searching has been extensively applied in text dataset considering the keywords as strings. String matching is used in finding all the occurrences of a given pattern P of length m from a given text T of length n, where m ≤ n. An occurrence of a pattern inside the text can simply be characterized as "exact" or "approximate". This paper proposes a framework for text mining using a fast bitparallel algorithm for searching exact occurrences of a pattern inside a huge body of text. We evaluate the performance of three algorithms in the literature on different text files and discuss their suitability under different situations.

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A review on parameterized string matching algorithms

Cited by 2 publications

References 8 publications

A General Approach to Uniformly Handle Different String Metrics Based on Heterogeneous Alphabets

A General Approach to Uniformly Handle Different String Metrics Based on Heterogeneous Alphabets

An Enhanced Text Mining Approach using Dynamic Programming

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