Abstract
Individual Pension System (IPS) is a personal future investment system that allows individuals to regularly save for their retirement.
IPS is enacted by the law and supported by the government through state contribution. In Turkey, IPS entered into force on October 27,
2003 and it achieved an impressive progress over the last years. This improvement has caused increase in amount of raw data stored in
databases. However, accumulated data are complicated and big to be processed and cannot be analyzed by classical methods. Data
mining is becoming an essential tool to discover hidden and potentially useful knowledge from raw data. For this reason, application of
data mining techniques on Individual Pension Savings and Investment system is necessary. In this study, one of the data mining
techniques, decision tree classification, was used to determine customers’ profile. SPSS Clementine 12.0 software was used to develop
a classification model. Analyses were performed by various decision tree algorithms. Some customer information of a pension
company operating in Turkey were extracted from system. The significant rules about customers were revealed by analysis. The results
of analysis indicated that the CHAID algorithm showed the best prediction with an accuracy of 85.64% among C5.0, C&R Tree, QUEST
References
- Apeh E., Gabrys B., Schierz A. 2014. Customer profile classification: To adapt classifiers or to Relabel customer profiles?, Neurocomputing 132, 3-13.
- Chen Y.-L., Chen J.-M., Tung C.-W. 2006. Data Mining Approach for Retail Knowledge Discovery with
- Consideration of the Effect of Shelf-Space Adjacency on Sales. Decision Support Systems 42, 1503–1520.
- Chien C.-F., Chen L.F. 2008. Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry. Expert systems with applications 34, 280-290.
- Han J., Kamber M., Pei J. 2012. Data Mining Concepts and Techniques. Morgan Kaufmann Publishers Inc., 3. Edition, Waltham, USA.
- Lee S., Lee S., Park Y. 2007. A prediction model for success of services in e-commerce using decision tree: E-customer’s attitude towards online service. Expert Systems with Applications 33, 572-581.
- Linoff G.S., Berry M.J. 2011. Data mining techniques: For marketing, sales and customer relationship management. (3rd ed.) Indianapolis, Wiley Publishing Inc.
- Prasad U.D., Madhavi S., 2012. Prediction of Churn Behavior Of Bank Customers Using Data Mining Tools. Business Intelligence Journal 5(1), 96.
- Tan P.-N., Steinbach M., Kumar V. 2006. Introduction to Data Mining. International Edition, Pearson Education Inc., Boston, USA.
- Yin Y., Kaku I., Tang J., Zhu J.M. 2011. Data Mining: Concepts, Methods and Applications in Management and Engineering Design.
Abstract
Individual Pension System (IPS) is a personal future investment system that allows individuals to regularly save for their retirement.
IPS is enacted by the law and supported by the government through state contribution. In Turkey, IPS entered into force on October 27,
2003 and it achieved an impressive progress over the last years. This improvement has caused increase in amount of raw data stored in
databases. However, accumulated data are complicated and big to be processed and cannot be analyzed by classical methods. Data
mining is becoming an essential tool to discover hidden and potentially useful knowledge from raw data. For this reason, application of
data mining techniques on Individual Pension Savings and Investment system is necessary. In this study, one of the data mining
techniques, decision tree classification, was used to determine customers’ profile. SPSS Clementine 12.0 software was used to develop
a classification model. Analyses were performed by various decision tree algorithms. Some customer information of a pension
company operating in Turkey were extracted from system. The significant rules about customers were revealed by analysis. The results
of analysis indicated that the CHAID algorithm showed the best prediction with an accuracy of 85.64% among C5.0, C&R Tree, QUEST.
References
- Apeh E., Gabrys B., Schierz A. 2014. Customer profile classification: To adapt classifiers or to Relabel customer profiles?, Neurocomputing 132, 3-13.
- Chen Y.-L., Chen J.-M., Tung C.-W. 2006. Data Mining Approach for Retail Knowledge Discovery with
- Consideration of the Effect of Shelf-Space Adjacency on Sales. Decision Support Systems 42, 1503–1520.
- Chien C.-F., Chen L.F. 2008. Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry. Expert systems with applications 34, 280-290.
- Han J., Kamber M., Pei J. 2012. Data Mining Concepts and Techniques. Morgan Kaufmann Publishers Inc., 3. Edition, Waltham, USA.
- Lee S., Lee S., Park Y. 2007. A prediction model for success of services in e-commerce using decision tree: E-customer’s attitude towards online service. Expert Systems with Applications 33, 572-581.
- Linoff G.S., Berry M.J. 2011. Data mining techniques: For marketing, sales and customer relationship management. (3rd ed.) Indianapolis, Wiley Publishing Inc.
- Prasad U.D., Madhavi S., 2012. Prediction of Churn Behavior Of Bank Customers Using Data Mining Tools. Business Intelligence Journal 5(1), 96.
- Tan P.-N., Steinbach M., Kumar V. 2006. Introduction to Data Mining. International Edition, Pearson Education Inc., Boston, USA.
- Yin Y., Kaku I., Tang J., Zhu J.M. 2011. Data Mining: Concepts, Methods and Applications in Management and Engineering Design.
Details
| Primary Language | English |
|---|---|
| Subjects | Engineering |
| Journal Section | Articles |
| Authors | |
| Publication Date | December 31, 2017 |
| Published in Issue | Year 2017 Issue: Özel Sayı - Special Issue |
