Relevant bibliographies by topics / Distance-based similarity measure

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
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Academic literature on the topic 'Distance-based similarity measure'

Author: Grafiati
Published: 4 June 2025
Last updated: 23 June 2025

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Journal articles on the topic "Distance-based similarity measure"

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Tang, Hui-Chin, and Shen-Tai Yang. "Counterintuitive Test Problems for Distance-Based Similarity Measures Between Intuitionistic Fuzzy Sets." Mathematics 7, no. 5 (2019): 437. http://dx.doi.org/10.3390/math7050437.

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This paper analyzes the counterintuitive behaviors of adopted twelve distance-based similarity measures between intuitionistic fuzzy sets. Among these distance-based similarity measures, the largest number of components of the distance in the similarity measure is four. We propose six general counterintuitive test problems to analyze their counterintuitive behaviors. The results indicate that all the distance-based similarity measures have some counterintuitive test problems. Furthermore, for the largest number of components of the distance-based similarity measure, four types of counterintuitive examples exist. Therefore, the counterintuitive behaviors are inevitable for the distance-based similarity measures between intuitionistic fuzzy sets.
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Sulaiman, Nor Hashimah, Daud Mohamad, Jamilah Mohd Shariff, Sharifah Aniza Sayed Ahmad, and Kamilah Abdullah. "Extended FTOPSIS with Distance and Set Theoretic-Based Similarity Measure." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 2 (2018): 387. http://dx.doi.org/10.11591/ijeecs.v9.i2.pp387-394.

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Comparing fuzzy numbers is an essential process in deducing the output of many fuzzy decision making methods. One of the comparison methods commonly used is by using similarity measure. The main advantage of the similarity measure over other approaches is its ability to minimize the loss of information in the computational process. Several similarity measures have been applied effectively in fuzzy decision making methods. In this paper, a new similarity measure based on the geometric distance, the center of gravity, Hausdorf distance and the set theoretic similarity formula known as the Dice similarity index are incorporated into the Extended Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) method particularly in calculating the closeness coefficients. This similarity measure is in favor of others as it is able to discriminate two similar shape fuzzy numbers effectively with two different locations. A validation process is carried out by implementing the proposed procedure of the Extended FTOPSIS with the new similarity measure in solving a supplier selection problem and the ranking outcome is then compared with the Extended FTOPSIS with other existing similarity measure. The result shows that the Extended FTOPSIS with the proposed similarity measure gives a consistent result without reducing any information in the computational process.
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Extended, FTOPSIS with Distance and Set Theoretic-Based Similarity Measure, Mohamad Daud, Mohd Shariff Jamilah, Aniza Sayed Ahmad Sharifah, and Abdullah Kamilah. "Extended FTOPSIS with Distance and Set Theoretic-Based Similarity Measure." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 2 (2018): 387–94. https://doi.org/10.11591/ijeecs.v9.i2.pp387-394.

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Comparing fuzzy numbers is an essential process in deducing the output of many fuzzy decision making methods. One of the comparison methods commonly used is by using similarity measure. The main advantage of the similarity measure over other approaches is its ability to minimize the loss of information in the computational process. Several similarity measures have been applied effectively in fuzzy decision making methods. In this paper, a new similarity measure based on the geometric distance, the center of gravity, Hausdorf distance and the set theoretic similarity formula known as the Dice similarity index are incorporated into the Extended Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) method particularly in calculating the closeness coefficients. This similarity measure is in favor of others as it is able to discriminate two similar shape fuzzy numbers effectively with two different locations. A validation process is carried out by implementing the proposed procedure of the Extended FTOPSIS with the new similarity measure in solving a supplier selection problem and the ranking outcome is then compared with the Extended FTOPSIS with other existing similarity measure. The result shows that the Extended FTOPSIS with the proposed similarity measure gives a consistent result without reducing any information in the computational process.
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Ren, Haiping, Shixiao Xiao, and Hui Zhou. "A Chi-square Distance-based Similarity Measure of Single-valued Neutrosophic Set and Applications." International Journal of Computers Communications & Control 14, no. 1 (2019): 78–89. http://dx.doi.org/10.15837/ijccc.2019.1.3430.

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The aim of this paper is to propose a new similarity measure of singlevalued neutrosophic sets (SVNSs). The idea of the construction of the new similarity measure comes from Chi-square distance measure, which is an important measure in the applications of image analysis and statistical inference. Numerical examples are provided to show the superiority of the proposed similarity measure comparing with the existing similarity measures of SVNSs. A weighted similarity is also put forward based on the proposed similarity. Some examples are given to show the effectiveness and practicality of the proposed similarity in pattern recognition, medical diagnosis and multi-attribute decision making problems under single-valued neutrosophic environment.
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Liu, Donghai, Guangyan Liu, and Zaiming Liu. "Some Similarity Measures of Neutrosophic Sets Based on the Euclidean Distance and Their Application in Medical Diagnosis." Computational and Mathematical Methods in Medicine 2018 (November 28, 2018): 1–9. http://dx.doi.org/10.1155/2018/7325938.

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Similarity measure is an important tool in multiple criteria decision-making problems, which can be used to measure the difference between the alternatives. In this paper, some new similarity measures of single-valued neutrosophic sets (SVNSs) and interval-valued neutrosophic sets (IVNSs) are defined based on the Euclidean distance measure, respectively, and the proposed similarity measures satisfy the axiom of the similarity measure. Furthermore, we apply the proposed similarity measures to medical diagnosis decision problem; the numerical example is used to illustrate the feasibility and effectiveness of the proposed similarity measures of SVNSs and IVNSs, which are then compared to other existing similarity measures.
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Qiang, He Qun, Chun Hua Qian, and Sheng Rong Gong. "Similarity Measure for Image Retrieval Based on Hausdorff Distance." Applied Mechanics and Materials 635-637 (September 2014): 1039–44. http://dx.doi.org/10.4028/www.scientific.net/amm.635-637.1039.

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In general, it is difficult to segment accurately image regions or boundary contours and represent them by feature vectors for shape-based image query. Therefore, the object similarity is often computed by their boundaries. Hausdorff distance is nonlinear for computing distance, it can be used to measure the similarity between two patterns of points of edge images. Classical Hausdorff measure need to express image as a feature matrix firstly, then calculate feature values or feature vectors, so it is time-consuming. Otherwise, it is difficult for part pattern matching when shadow and noise existed. In this paper, an algorithm that use Hausdorff distance on the image boundaries to measure similarity is proposed. Experimental result has showed that the proposed algorithm is robust.
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Zeng, Yanqiu, Haiping Ren, Tonghua Yang, Shixiao Xiao, and Neal Xiong. "A Novel Similarity Measure of Single-Valued Neutrosophic Sets Based on Modified Manhattan Distance and Its Applications." Electronics 11, no. 6 (2022): 941. http://dx.doi.org/10.3390/electronics11060941.

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A single-valued neutrosophic (SVN) set contains three parameters, which can well describe three aspects of an objective thing. However, most previous similarity measures of SVN sets often encounter some counter-intuitive examples. Manhattan distance is a well-known distance, which has been applied in pattern recognition, image analysis, ad-hoc wireless sensor networks, etc. In order to develop suitable distance measures, a new distance measure of SVN sets based on modified Manhattan distance is constructed, and a new distance-based similarity measure also is put forward. Then some applications of the proposed similarity measure are introduced. First, we introduce a pattern recognition algorithm. Then a multi-attribute decision-making method is proposed, in which a weighting method is developed by building an optimal model based on the proposed similarity measure. Furthermore, a clustering algorithm is also put forward. Some examples are also used to illustrate these methods.
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Zhang, Hailong, and Yongbin Zhou. "Mahalanobis Distance Similarity Measure Based Higher Order Optimal Distinguisher." Computer Journal 60, no. 8 (2017): 1131–44. http://dx.doi.org/10.1093/comjnl/bxw093.

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Zhang, Hailong, Yongbin Zhou, and Dengguo Feng. "Mahalanobis distance similarity measure based distinguisher for template attack." Security and Communication Networks 8, no. 5 (2014): 769–77. http://dx.doi.org/10.1002/sec.1033.

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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 string matching, namely Levenshtein distance, Jaro-Winkler distance, and Jaccard distance. Based on our evaluation, the combination of similarity measurements using textual and Wordnet similarity (conceptual) was the most optimal similarity method in food ingredients.
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Dissertations / Theses on the topic "Distance-based similarity measure"

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Nordström, Markus. "Automatic Source Code Classification : Classifying Source Code for a Case-Based Reasoning System." Thesis, Mittuniversitetet, Avdelningen för informations- och kommunikationssystem, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-25519.

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This work has investigated the possibility of classifying Java source code into cases for a case-based reasoning system. A Case-Based Reasoning system is a problem solving method in Artificial Intelligence that uses knowledge of previously solved problems to solve new problems. A case in case-based reasoning consists of two parts: the problem part and solution part. The problem part describes a problem that needs to be solved and the solution part describes how this problem was solved. In this work, the problem is described as a Java source file using words that describes the content in the source file and the solution is a classification of the source file along with the source code. To classify Java source code, a classification system was developed. It consists of four analyzers: type filter, documentation analyzer, syntactic analyzer and semantic analyzer. The type filter determines if a Java source file contains a class or interface. The documentation analyzer determines the level of documentation in asource file to see the usefulness of a file. The syntactic analyzer extracts statistics from the source code to be used for similarity, and the semantic analyzer extracts semantics from the source code. The finished classification system is formed as a kd-tree, where the leaf nodes contains the classified source files i.e. the cases. Furthermore, a vocabulary was developed to contain the domain knowledge about the Java language. The resulting kd-tree was found to be imbalanced when tested, as the majority of source files analyzed were placed inthe left-most leaf nodes. The conclusion from this was that using documentation as a part of the classification made the tree imbalanced and thus another way has to be found. This is due to the fact that source code is not documented to such an extent that it would be useful for this purpose.
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Goussakov, Roma. "Hellinger Distance-based Similarity Measures for Recommender Systems." Thesis, Umeå universitet, Statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-172385.

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Recommender systems are used in online sales and e-commerce for recommending potential items/products for customers to buy based on their previous buying preferences and related behaviours. Collaborative filtering is a popular computational technique that has been used worldwide for such personalized recommendations. Among two forms of collaborative filtering, neighbourhood and model-based, the neighbourhood-based collaborative filtering is more popular yet relatively simple. It relies on the concept that a certain item might be of interest to a given customer (active user) if, either he appreciated similar items in the buying space, or if the item is appreciated by similar users (neighbours). To implement this concept different kinds of similarity measures are used. This thesis is set to compare different user-based similarity measures along with defining meaningful measures based on Hellinger distance that is a metric in the space of probability distributions. Data from a popular database MovieLens will be used to show the effectiveness of dierent Hellinger distance-based measures compared to other popular measures such as Pearson correlation (PC), cosine similarity, constrained PC and JMSD. The performance of dierent similarity measures will then be evaluated with the help of mean absolute error, root mean squared error and F-score. From the results, no evidence were found to claim that Hellinger distance-based measures performed better than more popular similarity measures for the given dataset.
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Meghdadi, Amir Hossein. "Fuzzy Tolerance Neighborhood Approach to Image Similarity in Content-based Image Retrieval." 2012. http://hdl.handle.net/1993/8094.

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The main contribution of this thesis, is to define similarity measures between two images with the main focus on content-based image retrieval (CBIR). Each image is considered as a set of visual elements that can be described with a set of visual descriptions (features). The similarity between images is then defined as the nearness between sets of elements based on a tolerance and a fuzzy tolerance relation. A tolerance relation is used to describe the approximate nature of the visual perception. A fuzzy tolerance relation is adopted to eliminate the need for a sharp threshold and hence model the gradual changes in perception of similarities. Three real valued similarity measures as well as a fuzzy valued similarity measure are proposed. All of the methods are then used in two CBIR experiments and the results are compared with classical measures of distance (namely, Kantorovich, Hausdorff and Mahalanobis). The results are compared with other published research papers. An important advantage of the proposed methods is shown to be their effectiveness in an unsupervised setting with no prior information. Eighteen different features (based on color, texture and edge) are used in all the experiments. A feature selection algorithm is also used to train the system in choosing a suboptimal set of visual features.
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Book chapters on the topic "Distance-based similarity measure"

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Afify, Yasmine M., Ibrahim F. Moawad, Nagwa L. Badr, and Mohamed F. Tolba. "An Enhanced Distance Based Similarity Measure for User Based Recommendations." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48308-5_5.

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Hao, Zhinan, Zeshui Xu, and Hua Zhao. "The Decision Making Method Based on the New Distance Measure and Similarity Measure." In Uncertainty and Operations Research. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3891-9_2.

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Binh Nguyen, Ngoc, and Tu Bao Ho. "A Mixed Similarity Measure in Near-Linear Computational Complexity for Distance-Based Methods." In Principles of Data Mining and Knowledge Discovery. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45372-5_21.

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Nguyen, Thi Thuy Anh, and Stefan Conrad. "An Improved String Similarity Measure Based on Combining Information-Theoretic and Edit Distance Methods." In Communications in Computer and Information Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25840-9_15.

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Mardiana, Tari, Teguh Bharata Adji, and Indriana Hidayah. "The Comparation of Distance-Based Similarity Measure to Detection of Plagiarism in Indonesian Text." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46742-8_14.

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Thakur, Narina, Deepti Mehrotra, Abhay Bansal, and Manju Bala. "Analysis and Implementation of the Bray–Curtis Distance-Based Similarity Measure for Retrieving Information from the Medical Repository." In International Conference on Innovative Computing and Communications. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2354-6_14.

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Punzi, Giulia. "Continuous Interval Hamming Distance-Based Measures." In Algorithmic Foundations for Social Advancement. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-0668-9_8.

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Abstract The Continuous Interval Hamming distance (CIH) was introduced in 2010 in the context of detecting similarity for huge string data, such as genome sequences. Given two input strings, this metric provides a guarantee on the number of errors between each pair of aligned substrings of a given length k (called k-mers), while retaining a good definition of maximality. Indeed, the set of CIH-maximal substrings of two strings can be used to define maximal areas of similarity within a limited error ratio, which is hard to do with other widespread measures. Still, CIH has a major drawback: it has a low tolerance for insertion and deletion errors, which arise quite commonly in practical applications. With the aim of overcoming this issue, in this chapter we go a step beyond, introducing several novel similarity measures based on CIH-maximal substrings.
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Ruiz-Miró, Monica J., and Margaret Miró-Julià. "Dynamic Similarity and Distance Measures Based on Quantiles." In Computer Aided Systems Theory – EUROCAST 2015. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27340-2_11.

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Lesot, Marie-Jeanne, and Maria Rifqi. "Order-Based Equivalence Degrees for Similarity and Distance Measures." In Computational Intelligence for Knowledge-Based Systems Design. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14049-5_3.

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Ishii, Naohiro, Yusaku Tokuda, Ippei Torii, and Tomomi Kanda. "Similarity Grouping of Paintings by Distance Measure and Self Organizing Map." In Knowledge-Based and Intelligent Information and Engineering Systems. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04592-9_88.

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Conference papers on the topic "Distance-based similarity measure"

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Sturrock, C. P., and W. F. Bogaerts. "Classification and Prediction of Corrosion Phenomena via Cluster Analysis." In CORROSION 1996. NACE International, 1996. https://doi.org/10.5006/c1996-96383.

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Abstract The data that describe corrosion phenomena in terms of individual cases and feature values specify a mapping onto an abstract multidimensional feature space. Regions of high density, or clusters, signify areas of interest, and possibly, similar corrosion phenomena. This paper describes a method for calculating the similarity between cases based on a generalized measure of Euclidean distance. Evidence for a correlation between this similarity and observed corrosion phenomena is presented for a real-world database on chloride stress corrosion cracking (SCC) of Type 304 stainless steel in water. In contrast to conventional data analysis techniques, the method described can tolerate incomplete data. The performance of the method under different combinations of features was evaluated by calculating the error rate, which is the quotient of the erroneous predictions over the total number of cases examined. The error rate determined by considering incomplete data on pH and oxygen content in addition to complete data on temperature and chloride content was half that determined by considering these complete data alone. Additional data on evaporative service were found to correlate poorly with SCC behavior for the cases examined. These results illustrate the importance of feature selection in empirical modeling.
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Mohd, Wan Rosanisah Wan, and Lazim Abdullah. "Similarity measures of Pythagorean fuzzy sets based on combination of cosine similarity measure and Euclidean distance measure." In PROCEEDING OF THE 25TH NATIONAL SYMPOSIUM ON MATHEMATICAL SCIENCES (SKSM25): Mathematical Sciences as the Core of Intellectual Excellence. Author(s), 2018. http://dx.doi.org/10.1063/1.5041661.

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Gartner, D., F. Kraft, and T. Schaaf. "An Adaptive Distance Measure for Similarity Based Playlist Generation." In 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/icassp.2007.366658.

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Sun, Wenjie, Zheng Shan, Fudong Liu, Meng Qiao, Hairen Gui, and Xingwei Li. "Similarity Measure for Binary Function Based on Graph Mover’s Distance." In 2020 International Conference on Computer Vision, Image and Deep Learning (CVIDL). IEEE, 2020. http://dx.doi.org/10.1109/cvidl51233.2020.00-90.

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Zhao, Yanwei, Feng Zhang, Nan Su, Huijun Tang, and Jian Chen. "A Similarity Measure Based on Extension Distance and Its Application in CBR." In ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-68235.

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Case-based reasoning (CBR) is an effective method that integrates reasoning methodology and represents related knowledge in a domain. The success of a CBR system largely depends on case retrieval, and the similarity and determination of weight for each case features have a significant influence on the efficiency and accuracy of case retrieval. The aim of the research is to improve the efficiency and accuracy of case retrieval. Analyzing the deficiency of similarity measures based on the classical distance, different similarity measures are proposed for different kinds of attribute values based on the extension distance, especially the similarity model between numerical and set considered the customer’s preference. The standard deviation related with the similarity is introduced to distribute the dynamic attribute’s weights which also considered the customer’s interest, but not the traditional methods that the weight is a constant if determined. The presented methods will enable the system to retrieve the more similar case correctly so that reducing case adaptation. In this study, an electric drill is used as a case to verify the usefulness and effectiveness of the similarity measurements and weight assignments. It is demonstrated that this method is more beneficial to case retrieval compared with other methods.
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Yang, Huirong, Pengbin Fu, Baocai Yin, Mengduo Ma, and Yanyan Tang. "A Semantic Similarity Measure between Web Services Based on Google Distance." In 2011 IEEE 35th Annual Computer Software and Applications Conference - COMPSAC 2011. IEEE, 2011. http://dx.doi.org/10.1109/compsac.2011.97.

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Wang, Feng, Yuming Xiang, Xinghui Yao, and Jiayin Liu. "Shape Similarity Measure Method Based on Principal Curvature Enhancement Distance Transformation." In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2018. http://dx.doi.org/10.1109/igarss.2018.8517936.

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Yuankui Hu and Zengfu Wang. "A Similarity Measure Based on Hausdorff Distance for Human Face Recognition." In 18th International Conference on Pattern Recognition (ICPR'06). IEEE, 2006. http://dx.doi.org/10.1109/icpr.2006.174.

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Tran, Nicholas. "A perceptual similarity measure based on smoothing filters and the normalized compression distance." In IS&T/SPIE Electronic Imaging, edited by Bernice E. Rogowitz and Thrasyvoulos N. Pappas. SPIE, 2010. http://dx.doi.org/10.1117/12.845400.

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Ahmad, Sharifah Aniza Sayed, Daud Mohamad, Nor Hashimah Sulaiman, Jamilah Mohd Shariff, and Kamilah Abdullah. "A distance and set theoretic-based similarity measure for generalized trapezoidal fuzzy numbers." In PROCEEDING OF THE 25TH NATIONAL SYMPOSIUM ON MATHEMATICAL SCIENCES (SKSM25): Mathematical Sciences as the Core of Intellectual Excellence. Author(s), 2018. http://dx.doi.org/10.1063/1.5041574.

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Reports on the topic "Distance-based similarity measure"

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Tetzlaff, Sasha, Jinelle Sperry, and Brett DeGregorio. You can go your own way : no evidence for social behavior based on kinship or familiarity in captive juvenile box turtles. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/44923.

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Behavioral interactions between conspecific animals can be influenced by relatedness and familiarity. To test how kinship and familiarity influenced social behavior in juvenile Eastern Box Turtles (Terrapene carolina), 16 captive-born individuals were reared under semi-natural conditions in four equally sized groups, where each group comprised pairs of siblings and non-siblings. Using separation distance between pairs of turtles in rearing enclosures as a measure of gregariousness, we found no evidence suggesting siblings more frequently interacted with one another compared to non-relatives over the first five months of life. Average pair separation distance decreased during this time but may have been due to turtles aggregating around resources like heat and moist retreat areas as colder temperatures approached. At eight months old, we again measured repeated separation distances between unique pair combinations and similarly found no support for associations being influenced by kinship. Agonistic interactions between individuals were never observed. Based on our results, group housing and rearing of juvenile box turtles did not appear to negatively impact their welfare. Unlike findings for other taxa, our results suggest strategically housing groups of juvenile T. carolina to maintain social stability may not be an important husbandry consideration when planning releases of captive-reared individuals for conservation purposes.
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