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General Information
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2016 Vol.6(1): 32-35 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2016.6.1.567

Polygon Intersection Based Algorithm for Fuzzy Set Compatibility Calculations

Sukgamon Sukpisit, Supaporn Kansomkeat, Pannipa Sae Ueng, Apirada Thadadech, and Srđan Škrbić
Abstract—PFSQL is an extension of the SQL language that allows usage of fuzzy logic in SQL queries. In query statements, variables can take both fuzzy and non-fuzzy values. Normally, different types of values cannot be compared directly. Therefore, it is necessary to implement fuzzy compatibility calculation to solve this problem. This paper proposes a method of fuzzy compatibility calculation implementation that determines compatibility degree of two fuzzy sets. The compatibility value is calculated using polygon intersection algorithm. To prove the correctness of the proposed method, the application has been developed and tested with 360 compatibility cases of different randomly generated fuzzy values. The experimental results show that our algorithms can handle various types of intersections between any two fuzzy sets.

Index Terms—Compatibility, fuzzy database, fuzzy query, PFSQL.

S. Sukpisit, S. Kansomkeat, and A. Thadadech are with the Department of Computer Science, Faculty of Science, Prince of Songkla University, Songkhla, Thailand (e-mail: sukgamon.s@psu.ac.th, supaporn.k@psu.ac.th, apirada.t@psu.ac.th).
P. S. Ueng and S. Škrbić are with the Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad, Novi Sad, Serbia (e-mail: pannipa@dmi.uns.ac.rs, srdjan.skrbic@dmi.uns.ac.rs).

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Cite: Sukgamon Sukpisit, Supaporn Kansomkeat, Pannipa Sae Ueng, Apirada Thadadech, and Srđan Škrbić, "Polygon Intersection Based Algorithm for Fuzzy Set Compatibility Calculations," International Journal of Machine Learning and Computing vol.6, no. 1, pp. 32-35, 2016.

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