Analysis of a similarity measure for non-overlapped data

© 2017 by the authors. A similarity measure is a measure evaluating the degree of similarity between two fuzzy data sets and has become an essential tool in many applications including data mining, pattern recognition, and clustering. In this paper, we propose a similarity measure capable of handlin...

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Main Authors: Sanghyuk Lee, Jaehoon Cha, Nipon Theera-Umpon, Kyeong Soo Kim
Format: Journal
Published: 2018
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Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85019235664&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/46593
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-465932018-04-25T07:24:47Z Analysis of a similarity measure for non-overlapped data Sanghyuk Lee Jaehoon Cha Nipon Theera-Umpon Kyeong Soo Kim Computer Science Mathematics Agricultural and Biological Sciences © 2017 by the authors. A similarity measure is a measure evaluating the degree of similarity between two fuzzy data sets and has become an essential tool in many applications including data mining, pattern recognition, and clustering. In this paper, we propose a similarity measure capable of handling non-overlapped data as well as overlapped data and analyze its characteristics on data distributions. We first design the similarity measure based on a distance measure and apply it to overlapped data distributions. From the calculations for example data distributions, we find that, though the similarity calculation is effective, the designed similarity measure cannot distinguish two non-overlapped data distributions, thus resulting in the same value for both data sets. To obtain discriminative similarity values for non-overlapped data, we consider two approaches. The first one is to use a conventional similarity measure after preprocessing non-overlapped data. The second one is to take into account neighbor data information in designing the similarity measure, where we consider the relation to specific data and residual data information. Two artificial patterns of non-overlapped data are analyzed in an illustrative example. The calculation results demonstrate that the proposed similarity measures can discriminate non-overlapped data. 2018-04-25T06:57:37Z 2018-04-25T06:57:37Z 2017-05-01 Journal 20738994 2-s2.0-85019235664 10.3390/sym9050068 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85019235664&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/46593
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
topic Computer Science
Mathematics
Agricultural and Biological Sciences
spellingShingle Computer Science
Mathematics
Agricultural and Biological Sciences
Sanghyuk Lee
Jaehoon Cha
Nipon Theera-Umpon
Kyeong Soo Kim
Analysis of a similarity measure for non-overlapped data
description © 2017 by the authors. A similarity measure is a measure evaluating the degree of similarity between two fuzzy data sets and has become an essential tool in many applications including data mining, pattern recognition, and clustering. In this paper, we propose a similarity measure capable of handling non-overlapped data as well as overlapped data and analyze its characteristics on data distributions. We first design the similarity measure based on a distance measure and apply it to overlapped data distributions. From the calculations for example data distributions, we find that, though the similarity calculation is effective, the designed similarity measure cannot distinguish two non-overlapped data distributions, thus resulting in the same value for both data sets. To obtain discriminative similarity values for non-overlapped data, we consider two approaches. The first one is to use a conventional similarity measure after preprocessing non-overlapped data. The second one is to take into account neighbor data information in designing the similarity measure, where we consider the relation to specific data and residual data information. Two artificial patterns of non-overlapped data are analyzed in an illustrative example. The calculation results demonstrate that the proposed similarity measures can discriminate non-overlapped data.
format Journal
author Sanghyuk Lee
Jaehoon Cha
Nipon Theera-Umpon
Kyeong Soo Kim
author_facet Sanghyuk Lee
Jaehoon Cha
Nipon Theera-Umpon
Kyeong Soo Kim
author_sort Sanghyuk Lee
title Analysis of a similarity measure for non-overlapped data
title_short Analysis of a similarity measure for non-overlapped data
title_full Analysis of a similarity measure for non-overlapped data
title_fullStr Analysis of a similarity measure for non-overlapped data
title_full_unstemmed Analysis of a similarity measure for non-overlapped data
title_sort analysis of a similarity measure for non-overlapped data
publishDate 2018
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85019235664&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/46593
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