Abstract
Generally, data mining is the process of analyzing data from different viewpoint and summarizing it into valuable information. This area presents new theories and methods for processing large volumes of data and has obtained noteworthy consideration among researchers. In this paper, a new approach for decision-making process is developed based on the rough set theory of data mining and neural networks combined with data envelopment analysis method. The proposed procedure assesses the effect of personnel attributes on efficiency, utilizing DEA tool in estimating the efficiency of alternative decision making unites. By developing decision system, rough set theory is applied for feature selection (reducts) and all of plausible and meaningful ANN models are constructed for each reduct. Finally DEA method is used for selecting the best reduct and also most important personnel attributes for efficiency analysis. Persian bank branches employed for data generation and the characteristics of its personnel are analyzed on effectiveness of bank branches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Z. Pawlak, 1982, Rough sets, International Journal of Computer and Information Sciences, Vol 11, pp. 314–356.
Z. Pawlak, 1991, Rough Sets: Theoretical Aspects of Reasoning about Data. Boston, MA: Kluwer.
J. Stefanowski, 1997, Rough sets as a tool for studying attribute dependencies in the urinary stones treatment data set, in Rough Sets and Data Mining: Analysis and Imprecise Data, pp. 177–196.
Andrew Kusiak, 2000. Autonomous Decision-Making: A Data Mining Approach. 274 IEEE Transactions on Information Technology in biomedicine, Vol 4 Issue 4, pp. 274–284.
Weijun Xia, 2007, Supplier selection with multiple criteria in volume discount environments, Omega, Vol 35, Issue 5, October 2007, pp. 494–504.
Tzu-Liang, 2007, Rough set-based approach to feature selection in customer relationship management, Omega, Vol 35, Issue 4, pp.365–383.
Lian-Yin Zhai, 2002, Feature extraction using rough set theory and genetic algorithms-an application for the simplification of product quality evaluation, Computers & Industrial Engineering, Vol 43, Issue 4, pp.661–676.
Xiangyang Wang, 2006, Rough set feature selection and rule induction for prediction of malignancy degree in brain glioma Computer Methods and Programs in Biomedicine, Vol 83, Issue 2, pp.147–156.
Andrew Kusiak, 2000, Data mining in engineering design: a case study, IEEE International Conference on Robotics and Automation, Proceedings Vol 1, pp.206–211.
Wróblewski, 1998, Genetic algorithms in decomposition and classification problem, Physica-Verlag, Heidelberg, pp. 471–487.
Patuwo E., Hu M.Y., Hung M.S, 1993, Two-group classification using neural networks, Decision Sciences, Vol 24, Issue 4, pp. 825–845.
Moller, M. F., 1993, A scaled conjugate gradient algorithm for fast supervised learning,” Neural Networks, Vol. 6, pp. 525–533.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Azadeh, A., Javanmardi, L. (2008). Studying Impact of Decision Making Units Features on Efficiency by Integration of Data Envelopment Analysis and Data Mining Tools. In: Kalcsics, J., Nickel, S. (eds) Operations Research Proceedings 2007. Operations Research Proceedings, vol 2007. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77903-2_38
Download citation
DOI: https://doi.org/10.1007/978-3-540-77903-2_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77902-5
Online ISBN: 978-3-540-77903-2
eBook Packages: Business and EconomicsBusiness and Management (R0)