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Derivation of Fuzzy Rules from Interval-Valued Data

by Dmitri A. Viattchenin
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 7 - Number 3
Year of Publication: 2010
Authors: Dmitri A. Viattchenin
10.5120/1146-1500

Dmitri A. Viattchenin . Derivation of Fuzzy Rules from Interval-Valued Data. International Journal of Computer Applications. 7, 3 ( September 2010), 13-20. DOI=10.5120/1146-1500

@article{ 10.5120/1146-1500,
author = { Dmitri A. Viattchenin },
title = { Derivation of Fuzzy Rules from Interval-Valued Data },
journal = { International Journal of Computer Applications },
issue_date = { September 2010 },
volume = { 7 },
number = { 3 },
month = { September },
year = { 2010 },
issn = { 0975-8887 },
pages = { 13-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume7/number3/1146-1500/ },
doi = { 10.5120/1146-1500 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:55:26.131806+05:30
%A Dmitri A. Viattchenin
%T Derivation of Fuzzy Rules from Interval-Valued Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 7
%N 3
%P 13-20
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Fuzzy inference systems are widely used for classification and control. They can be designed from the training data. This paper describes a technique for deriving fuzzy classification rules from the interval-valued data. The technique based on a heuristic method of possibilistic clustering and a special method of the interval-valued data preprocessing. Basic concepts of the heuristic method of possibilistic clustering based on the allotment concept are described and the method of the interval-valued data preprocessing is also given. The method of constructing of fuzzy rules based on clustering results is presented. An illustrative example of the method’s application to the Sato and Jain’s interval-valued data is carried out. Preliminary conclusions are formulated.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Possibilistic clustering fuzzy cluster typical point tolerance threshold fuzzy classification rule interval-valued data

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