Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/56004
Title: A data mining approach for branch and ATM site evaluation
Authors: Shiu, SCK 
Liu, JNK
Lam, JLC
Feng, B
Keywords: Datamining
Rule induction
Branch and site evaluation
Model analysis
Issue Date: 2004
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2006, v. 3755, p. 303-318 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: In the past, some sites selected for closure by a large international bank in Hong Kong were based on personal experience of a group of experts by formulating a set of evaluation guidelines. The current 300 existing sites are therefore considered to represent a set of rules and expert decisions which are manually recorded on paper files and de-centralized. In order to validate the guidelines/rules and discover any hidden knowledge, we employ a data mining approach to examine the data comprehensively. Several modeling techniques including neural network, C5.0 and General Rule Induction systems are used to determine the significance of those attributes in the data set. Various models based on the historical data set of sites in different forms are constructed to deduce a rule-based model for subsequent use. Promising result has been obtained which can be applied in future Branch and ATM Site Evaluation with a view of providing a better solution. The useful patterns and knowledge discovered will further add benefit to exploring customer intelligence and devising marketing planning strategies.
Description: Eighth Pacific Asia Conference on Knowledge, Discovery and Data Mining, Carlton Crest Hotel, Sydney, 26-27 May, 2004
URI: http://hdl.handle.net/10397/56004
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/11677437_24
Appears in Collections:Conference Paper

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