Fuzzy set-theoretical approach for comparing objects with fuzzy attributes

Author

Bashon, Y. ; Neagu, D. ; Ridley, M.J.

Author_Institution

Dept. of Comput., Univ. of Bradford, Bradford, UK

fYear

2011

fDate

22-24 Nov. 2011

Firstpage

754

Lastpage

759

Abstract

In this paper we develop the similarity measure introduced by the Tversky contrast model and apply it on fuzzy sets using the cardinality of fuzzy sets and their operations. Based on this extended similarity definition we propose a new approach for comparing fuzzy objects and discuss some properties of the new similarity model. Some experimental examples are given to show the effectiveness of using this model against different cases. This work provides a method to compare objects with vague/fuzzy content and support further development of (fuzzy) data mining algorithms.

Keywords

data mining; fuzzy set theory; Tversky contrast model; cardinality; extended similarity definition; fuzzy attributes; fuzzy content; fuzzy data mining algorithms; fuzzy objects; fuzzy set-theoretical approach; fuzzy sets; similarity measure; similarity model; vague content; Computational modeling; Educational institutions; Equations; Finite element methods; Fuzzy sets; Intelligent systems; Mathematical model; Similarity measure; Tversky contrast model; fuzzy attributes; fuzzy objects;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems Design and Applications (ISDA), 2011 11th International Conference on

Conference_Location

Cordoba

ISSN

2164-7143

Print_ISBN

978-1-4577-1676-8

Type

conf

DOI

10.1109/ISDA.2011.6121747

Filename

6121747