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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 2012 Vol.2(5): 662-666 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.210

Credit Assessment of Bank Customers by a Fuzzy Expert System Based on Rules Extracted from Association Rules

Hamid Eslami Nosratabadi, Ahmad Nadali, and Sanaz Pourdarab
Abstract—Credit assessment is a very typical classification problem in Data Mining. A type of classification technique that has attracted an increasing number of attempts in recent years is finding classification rules based on association rule mining techniques. This paper aims to contribute to this kind of research by classifying the bank's customers via association rules with the use of the APRIORI algorithm and CRISP-DM methodology and considering the Experts' opinions to filter the obtained rules and define the Membership functions for the considered criteria, finally a Fuzzy Expert System is designed based on the selected rules from association rules to specify the Credit Degree of banks' customers. The presented steps have been studied in an Iranian Bank as empirical study.

Index Terms—Credit assessment, fuzzy expert system, classification, association rules.

The authors are with the Department of Information Technology Management, Science and Research Branch, Islamic Azad University, Tehran, Iran (Corresponding author’s email is Hamideslami.na@gmail.com; Nadali.ahmad@gmail.com; Pourdarab.sanaz@yahoo.com).

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Cite:Hamid Eslami Nosratabadi, Ahmad Nadali, and Sanaz Pourdarab, "Credit Assessment of Bank Customers by a Fuzzy Expert System Based on Rules Extracted from Association Rules," International Journal of Machine Learning and Computing vol.2, no. 5, pp. 662-666, 2012.

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