A Novel Similarity Measure for Generalized Trapezoidal Fuzzy Numbers and its Application to Decision-Making

Yixiang Zhou 보안공학연구지원센터 2016 International Journal of u- and e- Service, Scienc Vol.9 No.3

Similarity measures of fuzzy numbers have been widely applied in various areas. In the last decade, many similarity measures of generalized fuzzy numbers were proposed. However, there are two main limitations in existing similarity measures: 1) they cannot correctly calculate the degree of similarity between two generalized trapezoidal fuzzy numbers in some cases; and 2) the definitions of recently developed similarity measures are complicated and difficult to interpret. In this paper, a novel approach to similarity measurement between generalized trapezoidal fuzzy numbers is proposed. The proposed similarity measure has a simple definition and is easier to understand intuitively. Furthermore, we analyze its properties and compare it with existing similarity measures. The results show that the proposed measure outperforms existing similarity measures. Finally, we apply the proposed similarity measure to develop a fuzzy-logic-based approach for new product go/nogo decision-making at the front end. The proposed fuzzy software quality evaluation method is more flexible and more intelligent than existing methods due to the fact that it considers the degrees of confidence of evaluators’ opinions.

Fuzzy Entropy Construction based on Similarity Measure

Wook-Je Park,Park Hyun Jeong,Sang H. Lee 한국지능시스템학회 2007 한국지능시스템학회 학술발표 논문집 Vol.17 No.2

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.

Fuzzy Entropy Construction based on Similarity Measure

Park Hyun Jeong(박현정),Insuk Yang(양인석),Soorok Ryu(류수록),Sang H. Lee(이상혁) 한국지능시스템학회 2008 한국지능시스템학회논문지 Vol.18 No.2

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.

Analysis between Similarity and Dissimilarity Measure for Fuzzy Sets

이상혁(Sang-Hyuk Lee),김상진(Sangjin Kim),김채형(Jaehyung Kim) 한국지능시스템학회 2008 한국지능시스템학회 학술발표 논문집 Vol.18 No.2

In this paper, we have surveyed the relation between similarity measure and dissimilarity measure for fuzzy sets. First, we study the entropy for fuzzy set and similarity for corresponding crisp set. By the obtaining result, we pointed out that the similarity between fuzzy set and corresponding complementary fuzzy set satisfy fuzzy entropy. We also found out that the summation of similarity and dissimilarity measure between fuzzy set and complementary fuzzy set constitute total information of fuzzy set itself. With the obtained result we have extended the results to two data group fuzz sets. In the process of designing similarity measure and dissimilarity, we also proved the usefulness of proposed measures. We can also verified and discussed the one-to-one correspondence characteristics between similarity measure and dissimilarity measure(entropy).

C-Rank: A link-based similarity measure for scientific literature databases

Yoon, S.H.,Kim, S.W.,Park, S. Elsevier science 2016 Information sciences Vol.326 No.-

<P>As the number of people who use scientific literature databases has grown, the demand for literature retrieval services has steadily increased. One of the most popular retrieval service methods is to find a set of papers similar to the paper under consideration, which requires a measure that computes the similarities between the papers. Scientific literature databases exhibit two interesting characteristics that are not found in general databases. First, the papers cited by older papers are often not included in the database due to technical and economic reasons. Second, since a paper references previously published papers, few papers cite recently published papers. These two characteristics cause all existing similarity measures to fail in at least one of the following cases: (1) measuring the similarity between old, but similar papers, (2) measuring the similarity between recent, but similar papers, and (3) measuring the similarity between two similar papers: one old, the other recent. In this paper, we propose a new link-based similarity measure called C-Rank, which uses both in-link and out-link references, disregarding the direction of the references. In addition, we discuss the most suitable normalization method for scientific literature databases and we propose an evaluation method for measuring the accuracy of similarity measures. For the experiments, we used real-world papers from DBLP's database with reference information crawled from Libra. We then compared the performance of C-Rank with that of existing similarity measures. Experimental results showed that C-Rank achieved a higher accuracy than existing similarity measures. (C) 2015 Elsevier Inc. All rights reserved.</P>

Similarity measurement based on Min-Hash for Preserving Privacy

차현종,양호경,송유진 국제문화기술진흥원 2022 International Journal of Advanced Culture Technolo Vol.10 No.2

Because of the importance of the information, encryption algorithms are heavily used. Raw data is encrypted and secure, but problems arise when the key for decryption is exposed. In particular, large-scale Internet sites such as Facebook and Amazon suffer serious damage when user data is exposed. Recently, research into a new fourth-generation encryption technology that can protect user-related data without the use of a key required for encryption is attracting attention. Also, data clustering technology using encryption is attracting attention. In this paper, we try to reduce key exposure by using homomorphic encryption. In addition, we want to maintain privacy through similarity measurement. Additionally, holistic similarity measurements are time-consuming and expensive as the data size and scope increases. Therefore, Min-Hash has been studied to efficiently estimate the similarity between two signatures Methods of measuring similarity that have been studied in the past are time-consuming and expensive as the size and area of data increases. However, Min-Hash allowed us to efficiently infer the similarity between the two sets. Min-Hash is widely used for anti-plagiarism, graph and image analysis, and genetic analysis. Therefore, this paper reports privacy using homomorphic encryption and presents a model for efficient similarity measurement using Min-Hash.

사전 기술을 이용한 단어 유사도 측정 방안

최석재 국제언어인문학회 2009 인문언어 Vol.11 No.1

This paper aims to describe words similarity with intuition, objectivity, and measuring possibility.Measuring the words similarity have importance in that it gives us the standard with clearness. With the similarity standard we can decide which words could be called the ‘similar word’.But if we do not consider word's meaning in measuring the words similarity the result often does not comply with our intuition and not acceptable. So the measuring way should contain human intention which tells two words meaning are very close.To get the intention, dictionary's description were used. Because dictionaries are written by many experts in linguistics with accumulated efforts, we can regard it have proper description about words real meaning. So generally the descriptions comply with our intention. Descriptions were separated with words, and high frequency words were excluded. The result gives the words real meaning in forms of ‘word’.In addition to that, only the expressions which more than 2 dictionaries used are chosen. One dictionary some times can use unappropriated expressions, but if two or more dictionaries used the same expression, we can put higher trust on that expression. Consequently we get the objectivity.Finally when we use above results, we can draw its connection paths and calculate its similarity. When the two words are similar, they will have same expressions which tells its meaning. The same expressions, in other words ‘properties’ let the two words have connection path. And with this path, we clearly know they have similarity.When connection paths were drawn to all the words, the similarity can be counted. If two words have connection path directly we can say their similarity is 1st level, and if they are connected via another word we can say their similarity is 2nd level. In this way words similarity can be counted.And this paper designed the auto level-tracking program. It calculates the similarity level of two words. If there are some complements, the full process could be done automatically without human labor.

Similarity Measure Construction with Fuzzy Entropy and Distance Measure

Lee Sang-Hyuk Korean Institute of Intelligent Systems 2005 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.5 No.4

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.

신뢰성 있는 정보의 추출을 위한 퍼지집합의 유사측도 구성

이상혁,Lee Sang-Hyuk 한국통신학회 2005 韓國通信學會論文誌 Vol.30 No.9C

모호함의 측도를 위하여 퍼지 엔트로피와 거리측도 그리고 유사측도와의 관계를 이용하여 새로운 퍼지 측도를 제안하였다. 제안된 퍼지 엔트로피는 거리측도를 이용하여 구성된다. 거리측도는 일반적으로 사용되는 해밍 거리를 이용하였다. 또한 집합사이의 유사성을 측정하기 위한 유사측도를 거리 측도를 이용하여 구성하였고, 제안한 퍼지 엔트로피와 유사측도를 증명을 통하여 타당성을 확인하였다. 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.

Operations on the Similarity Measures of Fuzzy Sets

Omran, Saleh,Hassaballah, M. Korean Institute of Intelligent Systems 2007 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.7 No.3

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.