• Title, Summary, Keyword: Similarity

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Fuzzy Entropy Construction based on Similarity Measure (유사측도에 기반한 퍼지 엔트로피구성)

  • Park, Wook-Je;Park, Hyun-Jeong;Lee, Sang-H
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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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.

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A Max-Flow-Based Similarity Measure for Spectral Clustering

  • Cao, Jiangzhong;Chen, Pei;Zheng, Yun;Dai, Qingyun
    • 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.

Similarity Analysis Between Fuzzy Set and Crisp Set

  • Park, Hyun-Jeong;Lee, Sang-Hyuk.
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.7 no.4
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    • pp.295-300
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    • 2007
  • The similarity analysis for fuzzy set pair or crisp set pair are carried out. The similarity measure that is based on distance measure is derived and proved. The proposed similarity measure is considered with the help of analysis for uncertainty or certainty part of the membership functions. The usefulness of proposed similarity is verified through the computation of similarity between fuzzy set and crisp set or fuzzy set and fuzzy set. Our results are also compared with those of previous similarity measure which is based on fuzzy number.

A similarity measure of fuzzy sets

  • Kwon, Soon H.
    • Journal of the Korean Institute of Intelligent Systems
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    • v.11 no.3
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    • pp.270-274
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    • 2001
  • Conventional similarity measures suggested so far can be classified into three categories: (i) geometric similarity measures, (ij) set-theoretic similarity measures, and (iii) matching function-based similarity measures. On the basis of the characteristics of the conventional similarity measures, in this paper, we propose a new similarity measure of fuzzy sets and investigate its properLies. Finally, numelical examples are provided for the comparison of characteristics of the proposed similarity measure and other previous similarity measures.

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Similarity Measure Construction with Fuzzy Entropy and Distance Measure

  • Lee Sang-Hyuk
    • 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.

A New Class of Similarity Measures for Fuzzy Sets

  • Omran Saleh;Hassaballah M.
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.6 no.2
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    • pp.100-104
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    • 2006
  • Fuzzy techniques can be applied in many domains of computer vision community. The definition of an adequate similarity measure for measuring the similarity between fuzzy sets is of great importance in the field of image processing, image retrieval and pattern recognition. This paper proposes a new class of the similarity measures. The properties, sensitivity and effectiveness of the proposed measures are investigated and tested on real data. Experimental results show that these similarity measures can provide a useful way for measuring the similarity between fuzzy sets.

Empirical Comparison of Word Similarity Measures Based on Co-Occurrence, Context, and a Vector Space Model

  • Kadowaki, Natsuki;Kishida, Kazuaki
    • Journal of Information Science Theory and Practice
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    • v.8 no.2
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    • pp.6-17
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    • 2020
  • Word similarity is often measured to enhance system performance in the information retrieval field and other related areas. This paper reports on an experimental comparison of values for word similarity measures that were computed based on 50 intentionally selected words from a Reuters corpus. There were three targets, including (1) co-occurrence-based similarity measures (for which a co-occurrence frequency is counted as the number of documents or sentences), (2) context-based distributional similarity measures obtained from a latent Dirichlet allocation (LDA), nonnegative matrix factorization (NMF), and Word2Vec algorithm, and (3) similarity measures computed from the tf-idf weights of each word according to a vector space model (VSM). Here, a Pearson correlation coefficient for a pair of VSM-based similarity measures and co-occurrence-based similarity measures according to the number of documents was highest. Group-average agglomerative hierarchical clustering was also applied to similarity matrices computed by individual measures. An evaluation of the cluster sets according to an answer set revealed that VSM- and LDA-based similarity measures performed best.

Operations on the Similarity Measures of Fuzzy Sets

  • Omran, Saleh;Hassaballah, M.
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.7 no.3
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    • pp.205-208
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    • 2007
  • Measuring the similarity between fuzzy sets plays a vital role in several fields. However, none of all well-known similarity measure methods is all-powerful, and all have the localization of its usage. This paper defines some operations on the similarity measures of fuzzy sets such as summation and multiplication of two similarity measures. Also, these operations will be generalized to any number of similarity measures. These operations will be very useful especially in the field of computer vision, and data retrieval because these fields need to combine and find some relations between similarity measures.

Fuzzy Entropy Construction based on Similarity Measure

  • Park, Hyun-Jeong;Yang, In-Suk;Ryu, Soo-Rok;Lee, Sang-Hyuk
    • 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.

Multi-Modal Based Malware Similarity Estimation Method (멀티모달 기반 악성코드 유사도 계산 기법)

  • Yoo, Jeong Do;Kim, Taekyu;Kim, In-sung;Kim, Huy Kang
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.29 no.2
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    • pp.347-363
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    • 2019
  • Malware has its own unique behavior characteristics, like DNA for living things. To respond APT (Advanced Persistent Threat) attacks in advance, it needs to extract behavioral characteristics from malware. To this end, it needs to do classification for each malware based on its behavioral similarity. In this paper, various similarity of Windows malware is estimated; and based on these similarity values, malware's family is predicted. The similarity measures used in this paper are as follows: 'TF-IDF cosine similarity', 'Nilsimsa similarity', 'malware function cosine similarity' and 'Jaccard similarity'. As a result, we find the prediction rate for each similarity measure is widely different. Although, there is no similarity measure which can be applied to malware classification with high accuracy, this result can be helpful to select a similarity measure to classify specific malware family.