behavior and their point-of-sale (POS). The bank needs to know its merchants & apos; behavior to find
interesting ones to attract more transactions which results in the growth of its income and
assets. The recency, frequency and monetary (RFM) analysis is a famous approach for
extracting behavior of customers and is a basis for marketing and customer relationship
management (CRM), but it is not aligned enough for banking context. Introducing RF*M* in
this article results in a better understanding of groups of merchants. Another artifact of RF*M*
is RF*M* scoring which is applied in two ways, preprocessing the POSs and assigning
behavioral meaningful labels to the merchants’ segments. The class labels and the RF*M*
parameters are entered into a rule-based classification algorithm to achieve descriptive rules of
the clusters. These descriptive rules outlined the boundaries of RF*M* parameters for each
cluster. Since the rules are generated by a classification algorithm, they can also be applied for
predicting the behavioral label and scoring of the upcoming POSs. These rules are called
behavioral rules.
How to cite this paper
Bizhani, M & Tarokh, M. (2011). Behavioral rules of bank’s point-of-sale for segments description and scoring prediction.International Journal of Industrial Engineering Computations , 2(2), 337-350.
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Cheng, C.-H., & Chen, Y.-S. (2009). Classifying the segmentation of customer value via RFM model and RS theory. Expert Systems with Applications , 36(3), 4176-4184.
Engelbrecht, A. P. (2007). Computational Intelligence. John Wiley & Sons.
Garland, R. (2002). Non-financial drivers of customer profitability in personal retail banking. Journal of Targeting, Measurement and Analysis for Marketing , 10(3), 233-248.
Ha, S. H. (2007). Applying knowledge engineering techniques to customer analysis in the service industry. Advanced Engineering Informatics , 21(3), 293-301.
Halkidi, M., Batistakis, Y., & Vazirgiannis, M. (2001). On Clustering Validation Techniques. Journal of Intelligent Information System , 17(2-3), 107-143.
Han, J., & Kamber, M. (2006). Data Mining Concepts and Techniques. Morgan Kaufmann.
Hughes, A. M. (1994). Strategic Database Marketing. Chicago: Probus Publishing.
Humphrey, D. B., Kim, M., & Vale, B. (2001). Realizing the Gains from Electronic Payments: Costs, Pricing, and Payment Choice. Journal of Money, Credit and Banking , 33.
Jain, A., Murty, M., & Flynn, P. (1999). Data Clustering: A Review. ACM Computing Surveys , 31(3), 264-323.
Kohonen, T. (2001). Self- Organizing Maps (Third ed.). Springer.
Kotler, P., Wong, V., Saunders, J., & Armstrong, G. (2005). Principles of Marketing. Pearson Education.
Lughofer, E. (2008). Extensions of vector quantization for incremental clustering. Pattern Recognition , 41.
Romdhane, L., Fadhel, N., & Ayeb, B. (2010). An efficient approach for building customer profiles from business data. Expert Systems with Applications, 37(2), 1573-1585.
Rousseeuw, P. J. (1987). Silhouettes: a graphcal aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20.
Tan, P.-N., Steinbach, M., & Kumar, V. (2005). Introduction to Data Mining. Pearson Education.
Weka 3: Data Mining Software in Java. (n.d.). Retrieved from Weka 3: http://www.cs.waikato.ac.nz/ml/weka
Wu, K.-L., & Yang, M.-S. (2006). Alternative learning vector quantization. Pattern Recognition , 39.
Xu, R., & Wunsch, D. (2005). Survey of Clustering Algorithms. IEEE Transactions on Neural Networks , 16.
Yeh, I.-C., Yang, K.-J., & Ting, T.-M. (2009). Knowledge discovery on RFM model using Bernoulli sequence. Expert Systems with Applications, 36.