• 제목/요약/키워드: Measure

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클러스터 평가 외부기준 척도 $F_n$-Measure ($F_n$-Measure : An External Cluster Evaluation Measure)

  • 김경택
    • 산업경영시스템학회지
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    • 제35권4호
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    • pp.244-248
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    • 2012
  • F-Measure is one of the external measures for evaluating the validity of clustering results. Though it has clear advantages over other widely used external measures such as Purity and Entropy, F-Measure has inherently been less sensitive than other validity measures. This insensitivity owes to the definition of F-Measure that counts only most influential portions. In this research, we present $F_n$-Measure, an external cluster evaluation measure based on F-Measure. $F_n$-Measure is so sensitive that it can detect their difference in the cases that F-Measure cannot detect the difference in clustering results. We compare $F_n$-Measure to F-Measure for a few clustering results and show which measure draws better result based upon homogeneity and completeness.

Similarity Measure Construction with Fuzzy Entropy and Distance Measure

  • Lee Sang-Hyuk
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • 제5권4호
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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.

Relation between Certainty and Uncertainty with Fuzzy Entropy and Similarity Measure

  • Lee, Sanghyuk;Zhai, Yujia
    • 한국융합학회논문지
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    • 제5권4호
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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.

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

  • Park, Wook-Je;Park, Hyun-Jeong;Lee, Sang-H
    • 한국지능시스템학회:학술대회논문집
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    • 한국지능시스템학회 2007년도 추계학술대회 학술발표 논문집
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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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구간치 퍼지집합 상에서 쇼케이적분에 의해 정의된 거리측도와 유사측도에 관한 연구 (A note on distance measure and similarity measure defined by Choquet integral on interval-valued fuzzy sets)

  • 장이채
    • 한국지능시스템학회논문지
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    • 제17권4호
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    • pp.455-459
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    • 2007
  • Interval-valued fuzzy sets were suggested for the first time by Gorzafczany(1983) and Turksen(1986). Based on this, Zeng and Li(2006) introduced concepts of similarity measure and entropy on interval-valued fuzzy sets which are different from Bustince and Burillo(1996). In this paper, by using Choquet integral with respect to a fuzzy measure, we introduce distance measure and similarity measure defined by Choquet integral on interval-valued fuzzy sets and discuss some properties of them. Choquet integral is a generalization concept of Lebesgue inetgral, because the two definitions of Choquet integral and Lebesgue integral are equal if a fuzzy measure is a classical measure.

A NEW EXPONENTIAL DIRECTED DIVERGENCE INFORMATION MEASURE

  • JAIN, K.C.;CHHABRA, PRAPHULL
    • Journal of applied mathematics & informatics
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    • 제34권3_4호
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    • pp.295-308
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    • 2016
  • Depending upon the nature of the problem, different divergence measures are suitable. So it is always desirable to develop a new divergence measure. In the present work, new information divergence measure, which is exponential in nature, is introduced and characterized. Bounds of this new measure are obtained in terms of various symmetric and non- symmetric measures together with numerical verification by using two discrete distributions: Binomial and Poisson. Fuzzy information measure and Useful information measure corresponding to new exponential divergence measure are also introduced.

RELATION BETWEEN FRACTAL MEASURES AND CANTOR MEASURES

  • Baek, In-Soo
    • 대한수학회논문집
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    • 제22권2호
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    • pp.241-246
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    • 2007
  • We investigate the relation between Hausdorff(packing) measure and lower(packing) Cantor measure on a deranged Cantor set. If the infimum of some distortion of contraction ratios is positive, then Hausdorff(packing) measure and lower(packing) Cantor measure of a deranged Cantor set are equivalent except for some singular behavior for packing measure case. It is a generalization of already known result on the perturbed Cantor set.

Fuzzy Measure and Integration

  • Stojakovic, Mila
    • 한국지능시스템학회:학술대회논문집
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    • 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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    • pp.1418-1421
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    • 1993
  • The main purpose of this paper is to introduce and develop the notion of a fuzzy measure in separable Banach space. This definition of fuzzy measure is a natural generalization of the set-valued measure. Radon-Nikod m theorems for fuzzy measure are established.

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