Evaluating discovered rules from association rules mining based on interestingness measures using fuzzy expert system

Author

Nosratabadi, Hamid Eslami ; Nadali, Ahmad ; Pourdarab, Sanaz ; Khalilinezhad, Mahdieh

Author_Institution

Young Res. Club, Islamic Azad Univ., Tehran, Iran

fYear

2011

Firstpage

112

Lastpage

117

Abstract

Association rule mining is one of the popular pattern discovery methods in Knowledge Discovery in Databases (KDD). In this regard, all the obtained results from association rules are not useful and do not have same values. The aim of this paper is evaluating the extracted rules from data mining for bank credit customers. Here, a Fuzzy Expert System has been designed with considering interestingness measures as Input variables and Interestingness Rule level as output. Then, the system has been developed with the use of FIS tool of MATLAB software. This system is able to assess all the results of association rules according to the situation of each criterion. Finally, the presented steps have been run in an Iranian Bank as empirical study.

Keywords

banking; data mining; expert systems; mathematics computing; FIS tool; Iranian Bank; MATLAB software; association rules mining; bank credit customers; data mining; discovered rules evaluation; fuzzy expert system; interestingness measures; interestingness rule level; knowledge discovery in databases; pattern discovery methods; Analytical models; Computer languages; Data mining; Expert systems; Facsimile; Mathematical model; Reliability; Association Rules; Credit Scoring; Data Mining; Fuzzy Expert System; Interestingness Measure;

fLanguage

English

Publisher

ieee

Conference_Titel

Applications of Digital Information and Web Technologies (ICADIWT), 2011 Fourth International Conference on the

Conference_Location

Stevens Point, WI

Print_ISBN

978-1-4244-9824-6

Type

conf

DOI

10.1109/ICADIWT.2011.6041423

Filename

6041423