The week's pick
Reseach Article
Deposit subscribe Prediction using Data Mining Techniques based Real Marketing Dataset
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 110 - Number 3 |
| Year of Publication: 2015 |
| Authors: Safia Abbas |
10.5120/19293-0725
|
Safia Abbas . Deposit subscribe Prediction using Data Mining Techniques based Real Marketing Dataset. International Journal of Computer Applications. 110, 3 ( January 2015), 1-7. DOI=10.5120/19293-0725
Abstract
Recently, economic depression, which scoured all over the world, affects business organizations and banking sectors. Such economic pose causes a severe attrition for banks and customer retention becomes impossible. Accordingly, marketing managers are in need to increase marketing campaigns, whereas organizations evade both expenses and business expansion. In order to solve such riddle, data mining techniques is used as an uttermost factor in data analysis, data summarizations, hidden pattern discovery, and data interpretation. In this paper, rough set theory and decision tree mining techniques have been implemented, using a real marketing data obtained from Portuguese marketing campaign related to bank deposit subscription [Moro et al. , 2011]. The paper aims to improve the efficiency of the marketing campaigns and helping the decision makers by reducing the number of features, that describes the dataset and spotting on the most significant ones, and predict the deposit customer retention criteria based on potential predictive rules.
References
- Quinlan, J. R. C4. 5, "Programming for Machine Learnining" ,Morgan Kaufman Publishers, 1993.
- Turban, E. , Sharda, R. and Delen, D. , "Decision Support and Business Intelligence Systems", 9th edition, Prentice Hall Press, USA, 2013.
- Sérgio Moro and Raul M. S. , pulocortezlaureano, " Using Data Mining for Bank Direct Marketing:: An application of the CRISP-DM methdology ",In P. Novaiset al. (Eds. ), Proceedings of the European Simulation and Modelling Conference - ESM'2011, pp. 117-121, Guimarães, Portugal, October, 2011.
- J. Han and M. Kamber. "Data Mining: Concepts and Techniques", Morgan Kaufmann, Text book, 2000.
- Witten, I. and Frank, E. , "Data Mining – Pratical Machine Learning Tools and Techniques", 3rd edition, Elsevier, USA, 2005.
- Jiawei Han and MichelineKamber, Data Mining: Concepts and Techniques, text book, 2000.
- Serhat Ö. , A. Yilimza,"Classification and predication in adata mining application , Journal of Marmara for Pure and Applied Sciences, 18 159-174 ,Marmara University, Printed in Turkey, 2002.
- BinhThanh Luong, Salvatore Ruggieri, Franco Turini, k-NN as an Implementation of Situation Testing forDiscrimination Discovery and Prevention, KDD'11, San Diego, California, USA, 2011.
- Shailendra K. Shrivastava, ManishaTantuway, "A Decision Tree Algorithm based on Rough Set Theory after Dimensionality Reduction", International Journal of Computer Applications (0975 – 8887), Volume 17– No. 7, March 2011.
- Baoshi Ding, YongqingZheng, ShaoyuZang, A New Decision Tree Algorithm Based on Rough Set Theory, Asia-Pacific Conference on Information Processing, IEEE, 2009, pp. 326-329.
- Cuiru Wang and Fangfang OU, An Algorithm for Decision Tree Construction Based on Rough Set Theory, International Conference on Computer Science and Information Technology, IEEE 2008,pp. 295-299.
- http://hdl. handle. net/1822/14838
- http://www. cs. waikato. ac. nz/ml/weka/
- Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, Ian H. Witten, "The WEKA Data Mining Software: An Update", SIGKDD Explorations, Volume 11, Issue 1, page 10-19.
- Cios, Krzysztof J. , Witold, Pedrycz. , and Roman, W. Swiniarski. Data Mining Methods for Knowledge Discovery, Kluwer Academic publishers, boston/dordrecht/London, 1998.
- Zdzislaw P. , Rough sets, International Journal of Computer and Information Sciences, 11, 341-356, 1982.
- Zdzislaw P. , "Rough Sets-Theoretical Aspects and Reasoning about Data", Kluwer Academic Publications, 1991
- Binoy. B. Nair, V. P Mohandas, N. R. Sakthivel, "A Decision Tree- Rough Set Hybrid System for Stock Market Trend Prediction", International Journal of Computer Applications (0975 – 8887),Volume 6– No. 9, September 2010.
- NilimaPatil, RekhaLathi, Prof. RekhaLathi, "Comparison of C5. 0 & CART Classification algorithms using pruning technique", International Journal of Engineering Research & Technology (IJERT), Vol. 1 Issue 4, ISSN: 2278-0181, 2012.
- Hailong Sun, JinpengHuai, Yunhao Liu, RajkumarBuyya, "RCT: A distributed tree for supporting efficient range and multi-attribute queries in grid computing", Future Generation Computer Systems 24 (2008), 631–643.
- WANG Jue, WANG Ju, "Reduction Algorithms Based on Discernibility Matrix: The Ordered Attributes Method", J. Comput. Sci. & Technol. Vol. 16 No. 6, 2011.
- K. Chitra, B. Subashini, " Data Mining Techniques and its Applications in Banking Sector", International Journal of Emerging Technology and Advanced Engineering, ISSN 2250-2459, Volume 3, Issue 8, 2013.
Index Terms
Keywords