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: Lee S., Cha J., Theera-Umpon N., Kim K.
Format: Journal
Published: 2017
Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85019235664&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/40495
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-404952017-09-28T04:09:48Z Analysis of a similarity measure for non-overlapped data Lee S. Cha J. Theera-Umpon N. Kim K. © 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. 2017-09-28T04:09:48Z 2017-09-28T04:09:48Z 5 Journal 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/6653943832/40495
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
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 Lee S.
Cha J.
Theera-Umpon N.
Kim K.
spellingShingle Lee S.
Cha J.
Theera-Umpon N.
Kim K.
Analysis of a similarity measure for non-overlapped data
author_facet Lee S.
Cha J.
Theera-Umpon N.
Kim K.
author_sort Lee S.
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 2017
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85019235664&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/40495
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