Premium
On a similarity measure between LR‐type fuzzy numbers and its application to database acquisition
Author(s) -
Yang MiinShen,
Hung WenLiang,
ChangChien ShouJen
Publication year - 2005
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.20102
Subject(s) - similarity measure , measure (data warehouse) , data mining , similarity (geometry) , entropy (arrow of time) , computer science , fuzzy logic , metric (unit) , mathematics , theoretical computer science , database , artificial intelligence , image (mathematics) , operations management , physics , quantum mechanics , economics
This article presents a new similarity measure for LR‐type fuzzy numbers. The proposed similarity measure is based on a defined metric between LR‐type fuzzy numbers. It is known that an exponential operation is highly useful in dealing with the classical Shannon entropy and cluster analysis. We adopted, therefore, the exponential operation on this metric. Furthermore, we analyze its properties and make numerical comparisons to several similarity measures. The results show that the proposed similarity measure can overcome the drawbacks of the existing similarity measures. We then apply it to compound attributes for handling null queries to database systems. These applications can also be widely used in fuzzy queries to databases. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 1001–1016, 2005.