NDLI: Determination of rule weights of fuzzy association rules

Content Provider	IEEE Xplore Digital Library
Author	Ishibuchi, H. Yamamoto, T. Nakashima, T.
Copyright Year	2001
Description	Author affiliation: Dept. of Ind. Eng., Osaka Prefecture Univ., Japan (Ishibuchi, H.; Yamamoto, T.; Nakashima, T.)
Abstract	In this paper, first we extend two basic measures of association rules in data mining (i.e, confidence and support) to the case of fuzzy association rules. The main difference between standard and fuzzy association rules is the discretization of continuous variables. While continuous variables are divided into intervals for generating standard association rules, they are divided into linguistic values in the case of fuzzy association rules. Next we examine two specifications of rule weights of fuzzy association rules for pattern classification problems. One is the direct use of the confidence as a rule weight. The other is based on a slightly complicated formulation where the rule weight of each fuzzy association role is discounted by the confidence or other rules with the same antecedent conditions and different consequent classes. Through computer simulations on a pattern classification problem with many continuous attributes, we compare these two definitions with each other. Simulation results show that the direct use of the confidence is inferior to the other definition of rule weights. Then we examine three rule selection criteria (i.e., confidence, support, and their product). It is shown that good fuzzy association rules are extracted from numerical data using the product criterion. Finally we compare the performance of fuzzy association rules with that of standard association rules.
Starting Page	1555
Ending Page	1558
File Size	438268
Page Count	4
File Format	PDF
ISBN	078037293X
DOI	10.1109/FUZZ.2001.1008960
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-12-02
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Association rules Pattern classification Data mining Humans Fuzzy logic Fuzzy control Fuzzy sets Industrial engineering Neutron spin echo Computer simulation
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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