Academic literature on the topic 'String similarity measure'

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Journal articles on the topic "String similarity measure"

1

Revesz, Peter Z. "A Tiling Algorithm-Based String Similarity Measure." WSEAS TRANSACTIONS ON COMPUTER RESEARCH 9 (August 10, 2021): 109–12. http://dx.doi.org/10.37394/232018.2021.9.13.

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This paper describes a similarity measure for strings based on a tiling algorithm. The algorithm is applied to a pair of proteins that are described by their respective amino acid sequences. The paper also describes how the algorithm can be used to find highly conserved amino acid sequences and examples of horizontal gene transfer between different species
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Al-Bakry, Abbas, and Marwa Al-Rikaby. "Enhanced Levenshtein Edit Distance Method functioning as a String-to-String Similarity Measure." Iraqi Journal for Computers and Informatics 42, no. 1 (2016): 48–54. http://dx.doi.org/10.25195/ijci.v42i1.83.

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Levenshtein is a Minimum Edit Distance method; it is usually used in spell checking applications for generatingcandidates. The method computes the number of the required edit operations to transform one string to another and it canrecognize three types of edit operations: deletion, insertion, and substitution of one letter. Damerau modified the Levenshteinmethod to consider another type of edit operations, the transposition of two adjacent letters, in addition to theconsidered three types. However, the modification suffers from the time complexity which was added to the original quadratictime
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Sakunthala Prabha, K. S., C. Mahesh, and S. P. Raja. "An Enhanced Semantic Focused Web Crawler Based on Hybrid String Matching Algorithm." Cybernetics and Information Technologies 21, no. 2 (2021): 105–20. http://dx.doi.org/10.2478/cait-2021-0022.

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Abstract Topic precise crawler is a special purpose web crawler, which downloads appropriate web pages analogous to a particular topic by measuring cosine similarity or semantic similarity score. The cosine based similarity measure displays inaccurate relevance score, if topic term does not directly occur in the web page. The semantic-based similarity measure provides the precise relevance score, even if the synonyms of the given topic occur in the web page. The unavailability of the topic in the ontology produces inaccurate relevance score by the semantic focused crawlers. This paper overcome
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Rakhmawati, Nur Aini, and Miftahul Jannah. "Food Ingredients Similarity Based on Conceptual and Textual Similarity." Halal Research Journal 1, no. 2 (2021): 87–95. http://dx.doi.org/10.12962/j22759970.v1i2.107.

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Open Food Facts provides a database of food products such as product names, compositions, and additives, where everyone can contribute to add the data or reuse the existing data. The open food facts data are dirty and needs to be processed before storing the data to our system. To reduce redundancy in food ingredients data, we measure the similarity of ingredient food using two similarities: the conceptual similarity and textual similarity. The conceptual similarity measures the similarity between the two datasets by its word meaning (synonym), while the textual similarity is based on fuzzy st
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Znamenskij, Sergej Vital'evich. "Stable assessment of the quality of similarity algorithms of character strings and their normalizations." Program Systems: Theory and Applications 9, no. 4 (2018): 561–78. http://dx.doi.org/10.25209/2079-3316-2018-9-4-561-578.

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The choice of search tools for hidden commonality in the data of a new nature requires stable and reproducible comparative assessments of the quality of abstract algorithms for the proximity of symbol strings. Conventional estimates based on artificially generated or manually labeled tests vary significantly, rather evaluating the method of this artificial generation with respect to similarity algorithms, and estimates based on user data cannot be accurately reproduced. A simple, transparent, objective and reproducible numerical quality assessment of a string metric. Parallel texts of book tra
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Setiawan, Rudi. "Similarity Checking Similarity Checking of Source Code Module Using Running Karp Rabin Greedy String Tiling." Science Proceedings Series 1, no. 2 (2019): 43–46. http://dx.doi.org/10.31580/sps.v1i2.624.

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 Similarity checking of source code module, required a long process if it is done manually. Based on that problem, this research designed a software with structure-based approach using string matching technique with Running Karp-Rabin Greedy String Tiling (RKR-GST) Algorithm to check the similarity and using Dice Coefficient method to measure the level of similarity from 2 results source code modules.
 
 
 
 The result of the experiments show that RKRGST which applied in this system capable of recognizing the changing of statement and the changing statem
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7

RODRIGUEZ, WLADIMIR, MARK LAST, ABRAHAM KANDEL, and HORST BUNKE. "GEOMETRIC APPROACH TO DATA MINING." International Journal of Image and Graphics 01, no. 02 (2001): 363–86. http://dx.doi.org/10.1142/s0219467801000220.

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In this paper, a new, geometric approach to pattern identification in data mining is presented. It is based on applying string edit distance computation to measuring the similarity between multi-dimensional curves. The string edit distance computation is extended to allow the possibility of using strings, where each element is a vector rather than just a symbol. We discuss an approach for representing 3D-curves using the curvature and the tension as their symbolic representation. This transformation preserves all the information contained in the original 3D-curve. We validate this approach thr
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Samanta, Soumitra, Steve O’Hagan, Neil Swainston, Timothy J. Roberts, and Douglas B. Kell. "VAE-Sim: A Novel Molecular Similarity Measure Based on a Variational Autoencoder." Molecules 25, no. 15 (2020): 3446. http://dx.doi.org/10.3390/molecules25153446.

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Molecular similarity is an elusive but core “unsupervised” cheminformatics concept, yet different “fingerprint” encodings of molecular structures return very different similarity values, even when using the same similarity metric. Each encoding may be of value when applied to other problems with objective or target functions, implying that a priori none are “better” than the others, nor than encoding-free metrics such as maximum common substructure (MCSS). We here introduce a novel approach to molecular similarity, in the form of a variational autoencoder (VAE). This learns the joint distribut
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Zhu, Jin, Dayu Cheng, Weiwei Zhang, Ci Song, Jie Chen, and Tao Pei. "A New Approach to Measuring the Similarity of Indoor Semantic Trajectories." ISPRS International Journal of Geo-Information 10, no. 2 (2021): 90. http://dx.doi.org/10.3390/ijgi10020090.

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People spend more than 80% of their time in indoor spaces, such as shopping malls and office buildings. Indoor trajectories collected by indoor positioning devices, such as WiFi and Bluetooth devices, can reflect human movement behaviors in indoor spaces. Insightful indoor movement patterns can be discovered from indoor trajectories using various clustering methods. These methods are based on a measure that reflects the degree of similarity between indoor trajectories. Researchers have proposed many trajectory similarity measures. However, existing trajectory similarity measures ignore the ind
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Sabarish, B. A., Karthi R., and Gireesh Kumar T. "String-Based Feature Representation for Trajectory Clustering." International Journal of Embedded and Real-Time Communication Systems 10, no. 2 (2019): 1–18. http://dx.doi.org/10.4018/ijertcs.2019040101.

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A trajectory is the spatial trail of a moving object as a function of time. All moving objects such as humans, robots, cloud, taxis, animals, mobile phones generate trajectories. Trajectory clustering is grouping of trajectories that have similar moving patterns, and the formed clusters depend on feature representation, similarity metrics, and clustering algorithm used. In this article, trajectory features are generated after mapping trajectories onto grids, as this smoothens the variations that occur in spatial coordinates. These variations occur due to differences in how GPS points at varyin
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