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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
We’re sorry, something doesn't seem to be working properly.
Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Koch, T.W., MacDonald, S.S.: Bank Management, 5th edn. South-Weastern College Pub. (2002)
Keim, D.A.: Information visualization and visual data mining. IEEE Transactions on Visualization and Computer Graphics 8(1), 1–8 (2002)
Liu, J.N.K., Sin, D.K.Y.: A datamining approach for maintenance scheduling. International Journal of Engineering Intelligent Systems, 119–126 (2000)
Pazzani, M.J.: Knowledge discovery from data? IEEE Intelligent Systems and Their Applications 15(2), 10–12 (2000)
Wu, X.D.: Data mining: artificial intelligence in data analysis. In: IEEE/WIC/ACM International Conference on Intelligent Agent Technology, p. 7 (2004)
Standard Chartered Bank, http://www.standardchartered.com/global/index.html
Liu, J.N.K., Leung, F.: A framework to investigate consumer preference on using new interactive media for electronic Banking. In: International Conference on Electronic Commerce, pp. 28–34 (2000)
Durkee, D.P., Pohl, E.A., Mykytka, E.F.: Input data characterization factors for complex systems affecting availability estimation accuracy. In: Annual Symposium on Reliability and Maintainability, pp. 80–89 (2002)
Chung, S.M., Mangamuri, M.: Mining association rules from relations on a parallel NCR teradata database system. In: International Conference on Information Technology: Coding and Computing, vol. 1, pp. 465–470 (2004)
Jin, D., Ziavras, S.G.: A super-programming approach for mining association rules in parallel on PC clusters. IEEE Transactions on Parallel and Distributed Systems 15(9), 783–794 (2004)
Shi, H., Zhang, J.F., Zheng, L.: Mining association rule oriented data cube and its application. In: International Conference on Machine Learning and Cybernetics, vol. 2, pp. 705–709 (2002)
Yen, S.J., Chen, A.L.P.: A graph-based approach for discovering various types of association rules. IEEE Transactions on Knowledge and Data Engineering 13(5), 839–845 (2001)
Jia, X.P., Richards, J.A.: Cluster-space representation for hyperspectral data classification. IEEE Transactions on Geoscience and Remote Sensing 40(3), 593–598 (2002)
Bin, X., Aimeur, E., Fernandez, J.M.: PCFinder: an intelligent product recommendation agent for e-commerce. In: IEEE International Conference on E-Commerce, pp. 181–188 (2003)
Wang, H.X., Perng, C.S., Fan, W., Yu, P.S.: An index structure for pattern similarity searching in DNA microarray data. In: IEEE Computer Society on Bioinformatics Conference, pp. 256–267 (2002)
Angelov, P.P., Filev, D.P.: Flexible models with evolving structure. In: International IEEE Symposium on Intelligent Systems, vol. 2, pp. 28–33 (2002)
You, J., Liu, J., Li, L., Cheung, K.H.: On data mining and data warehousing for multimedia information retrieval. In: IASTED International Conference on Artificial and Computational Intelligence, pp. 130–135 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Shiu, S.C.K., Liu, J.N.K., Lam, J.L.C., Feng, B. (2006). A Data Mining Approach for Branch and ATM Site Evaluation. In: Williams, G.J., Simoff, S.J. (eds) Data Mining. Lecture Notes in Computer Science(), vol 3755. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11677437_24
Download citation
DOI: https://doi.org/10.1007/11677437_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32547-5
Online ISBN: 978-3-540-32548-2
eBook Packages: Computer ScienceComputer Science (R0)