• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.366-369
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• 2007

In this paper we derived fuzzy entropy that is based on similarity measure. Similarity measure represents the degree of similarity between two informations, those informations characteristics are not important. First we construct similarity measure between two informations, and derived entropy functions with obtained similarity measure. Obtained entropy is verified with proof. With the help of one-to-one similarity is also obtained through distance measure, this similarity measure is also proved in our paper.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.4
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• pp.367-371
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• 2005

The similarity measure is derived using fuzzy entropy and distance measure. By the elations of fuzzy entropy, distance measure, and similarity measure, we first obtain the fuzzy entropy. And with both fuzzy entropy and distance measure, similarity measure is obtained., We verify that the proposed measure become the similarity measure.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.257-261
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• 2008

In this Paper we derived fuzzy entropy that is based on similarity measure. Similarity measure represents the degree of similarity between two informations, those informations characteristics are not important. First we construct similarity measure between two informations, and derived entropy functions with obtained similarity measure. Obtained entropy is verified with proof. With the help of one-to-one similarity is also obtained through distance measure, this similarity measure is also proved in our paper.

• Journal of the Korea Convergence Society
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• v.5 no.4
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• pp.155-161
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• 2014

We survey the relation of fuzzy entropy measure and similarity measure. Each measure represents features of data uncertainty and certainty between comparative data group. With the help of one-to-one correspondence characteristics, distance measure and similarity measure have been expressed by the complementary characteristics. We construct similarity measure using distance measure, and verification of usefulness is proved. Furthermore analysis of similarity measure from fuzzy entropy measure is also discussed.

• Journal of the Korea Convergence Society
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• v.4 no.3
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• pp.35-41
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• 2013

Grouping problem with similarities between DNA sequences are studied. The similaritymeasure and the distance measure showed the complementary characteristics. Distance measure can be obtained by complementing similarity measure, and vice versa. Similarity measure is derived and proved. Usefulness of the proposed similarity measure is applied to grouping problem of 25 cockroach DNA sequences. By calculation of DNA similarity, 25 cockroaches are clustered by four groups, and the results are compared with the previous neighbor-joining method.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.200-205
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• 2008

For data analysis, fuzzy entropy is introduced as the measure of fuzziness, similarity measure is also constructed to represent similarity between data. Similarity measure between fuzzy membership functions is constructed through distance measure, and the proposed similarity measure are proved. Application of proposed similarity measure to the example of reliable data selection is also carried out. Application results are compared with the previous results that is obtained through fuzzy entropy and statistical knowledge.

• Journal of the Korean Institute of Telematics and Electronics
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• v.20 no.6
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• pp.23-28
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• 1983

Two measures of similarity between contours, the 1 st-order similarity measure and the 2nd-order similarity measure are proposed. They are based on the residual errors of the least squares fit. In particular, the 2nd-order similarity measure has a good reliability with respect to contours of many variations such as imperfection, affine transform or combination of these properties. By taking experiments of aircraft identification and recognition we show that in the matching performance the 2nd -order similarity measure is superior not only to the 1 st-order similarity measure but also to the previous matching techniques.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.199-202
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• 2007

The similarity measure is constructed for non-convex fuzzy membership function using well known Hamming distance measure. Comparison with convex fuzzy membership function is carried out, furthermore characteristic analysis for non-convex function are also illustrated. Proposed similarity measure is proved and the usefulness is verified through example. In example, usefulness of proposed similarity is pointed out.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.9C
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• pp.854-859
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• 2005

We construct the fuzzy entropy for measuring of uncertainty with the help of relation between distance measure and similarity measure. Proposed fuzzy entropy is constructed through distance measure. In this study, the distance measure is used Hamming distance measure. Also for the measure of similarity between fuzzy sets or crisp sets, we construct similarity measure through distance measure, and the proposed 려zzy entropies and similarity measures are proved.

• ETRI Journal
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• v.35 no.2
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• pp.311-320
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• 2013

In most spectral clustering approaches, the Gaussian kernel-based similarity measure is used to construct the affinity matrix. However, such a similarity measure does not work well on a dataset with a nonlinear and elongated structure. In this paper, we present a new similarity measure to deal with the nonlinearity issue. The maximum flow between data points is computed as the new similarity, which can satisfy the requirement for similarity in the clustering method. Additionally, the new similarity carries the global and local relations between data. We apply it to spectral clustering and compare the proposed similarity measure with other state-of-the-art methods on both synthetic and real-world data. The experiment results show the superiority of the new similarity: 1) The max-flow-based similarity measure can significantly improve the performance of spectral clustering; 2) It is robust and not sensitive to the parameters.