The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Dans cette étude, nous avons étudié la relation entre les mesures de similarité et l'entropie pour les ensembles flous. Premièrement, nous avons développé l’entropie floue en utilisant la mesure de distance pour les ensembles flous. Nous avons souligné que la distance entre l’ensemble flou et l’ensemble net correspondant est égale à l’entropie floue. Nous avons également constaté que la somme de la mesure de similarité et de l'entropie entre l'ensemble flou et l'ensemble net correspondant constitue l'information totale dans l'ensemble flou. Enfin, nous avons dérivé une mesure de similarité à partir de l'entropie et avons montré par un exemple simple que la mesure de similarité maximale peut être obtenue en utilisant une formulation d'entropie minimale.
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Sang-Hyuk LEE, Keun Ho RYU, Gyoyong SOHN, "Study on Entropy and Similarity Measure for Fuzzy Set" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 9, pp. 1783-1786, September 2009, doi: 10.1587/transinf.E92.D.1783.
Abstract: In this study, we investigated the relationship between similarity measures and entropy for fuzzy sets. First, we developed fuzzy entropy by using the distance measure for fuzzy sets. We pointed out that the distance between the fuzzy set and the corresponding crisp set equals fuzzy entropy. We also found that the sum of the similarity measure and the entropy between the fuzzy set and the corresponding crisp set constitutes the total information in the fuzzy set. Finally, we derived a similarity measure from entropy and showed by a simple example that the maximum similarity measure can be obtained using a minimum entropy formulation.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.1783/_p
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@ARTICLE{e92-d_9_1783,
author={Sang-Hyuk LEE, Keun Ho RYU, Gyoyong SOHN, },
journal={IEICE TRANSACTIONS on Information},
title={Study on Entropy and Similarity Measure for Fuzzy Set},
year={2009},
volume={E92-D},
number={9},
pages={1783-1786},
abstract={In this study, we investigated the relationship between similarity measures and entropy for fuzzy sets. First, we developed fuzzy entropy by using the distance measure for fuzzy sets. We pointed out that the distance between the fuzzy set and the corresponding crisp set equals fuzzy entropy. We also found that the sum of the similarity measure and the entropy between the fuzzy set and the corresponding crisp set constitutes the total information in the fuzzy set. Finally, we derived a similarity measure from entropy and showed by a simple example that the maximum similarity measure can be obtained using a minimum entropy formulation.},
keywords={},
doi={10.1587/transinf.E92.D.1783},
ISSN={1745-1361},
month={September},}
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TY - JOUR
TI - Study on Entropy and Similarity Measure for Fuzzy Set
T2 - IEICE TRANSACTIONS on Information
SP - 1783
EP - 1786
AU - Sang-Hyuk LEE
AU - Keun Ho RYU
AU - Gyoyong SOHN
PY - 2009
DO - 10.1587/transinf.E92.D.1783
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2009
AB - In this study, we investigated the relationship between similarity measures and entropy for fuzzy sets. First, we developed fuzzy entropy by using the distance measure for fuzzy sets. We pointed out that the distance between the fuzzy set and the corresponding crisp set equals fuzzy entropy. We also found that the sum of the similarity measure and the entropy between the fuzzy set and the corresponding crisp set constitutes the total information in the fuzzy set. Finally, we derived a similarity measure from entropy and showed by a simple example that the maximum similarity measure can be obtained using a minimum entropy formulation.
ER -