IEICE TRANSACTIONS on Information
Study on Entropy and Similarity Measure for Fuzzy Set
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E92-D No.9 pp.1783-1786
- Publication Date
- 2009/09/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E92.D.1783
- Type of Manuscript
- LETTER
- Category
- Computation and Computational Models
Authors
Keyword
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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},}
Copy
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 -
English
