CFP last date
01 April 2024
Reseach Article

A Naïve Hopfield Neural Network based Approach for Multiclass Classification of Customer Loyalty

by Pooja Agarwal, Surya Prasad, J., Arti Arya
Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 2 - Number 5
Year of Publication: 2015
Authors: Pooja Agarwal, Surya Prasad, J., Arti Arya
10.5120/cae2015651753

Pooja Agarwal, Surya Prasad, J., Arti Arya . A Naïve Hopfield Neural Network based Approach for Multiclass Classification of Customer Loyalty. Communications on Applied Electronics. 2, 5 ( July 2015), 36-43. DOI=10.5120/cae2015651753

@article{ 10.5120/cae2015651753,
author = { Pooja Agarwal, Surya Prasad, J., Arti Arya },
title = { A Naïve Hopfield Neural Network based Approach for Multiclass Classification of Customer Loyalty },
journal = { Communications on Applied Electronics },
issue_date = { July 2015 },
volume = { 2 },
number = { 5 },
month = { July },
year = { 2015 },
issn = { 2394-4714 },
pages = { 36-43 },
numpages = {9},
url = { https://www.caeaccess.org/archives/volume2/number5/395-2015651753/ },
doi = { 10.5120/cae2015651753 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-09-04T19:40:11.865875+05:30
%A Pooja Agarwal
%A Surya Prasad
%A J.
%A Arti Arya
%T A Naïve Hopfield Neural Network based Approach for Multiclass Classification of Customer Loyalty
%J Communications on Applied Electronics
%@ 2394-4714
%V 2
%N 5
%P 36-43
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Customer classification is an area of utmost interest for all businesses. For any organization retaining customer is more important than making new customers. In this paper, a simple idea based on Hopfield Neural Network (HNN) is proposed for multiclass classification of customer loyalty. Initially, transformation and k-medoid clustering algorithm preprocesses the training example dataset. Then, classifier model (HNN) learns patterns from this training set. After training is done, patterns are stored and classifier is ready to classify the unclassified examples using weighted matrices and Euclidean norm. It learns from its environment and does not need to be reprogrammed. The proposed classifier is tested over a real dataset collected through an online survey and it is 87.5% accurate, which is an encouraging result.

References
  1. Arya A., Agarwal P., Dangeti A., Pajaan A., Suresh A., Praveena S. 2010. Automatic Fuzzy Classification tool for Customer Loyalty using Gaussian Membership Function. In Ciit Intl. J. of Data Mining and Knowledge Engineering.
  2. Arya A., Agarwal P. 2010. Fuzzy Decision Tree based Automatic Classifier for Customer Loyalty. In Proc. Of Intl. Conf. on Data Management.
  3. Wei-Kun Y., Mei-Hua Z., Jian M. 2007. Value-based Customer Loyalty Evolution. Service Operations and Logistics and Informatics, IEEE International Conference.
  4. Alice Julien- Laferriere “Hopfield Network”
  5. https://www4.rgu.ac.uk/files/chapter7-hopfield.pdf
  6. Ranilla J., Luaces O., Bahamonde A. 2003. A Heuristic for Learning Decision Trees and Pruning them into Classification Rules. Artificial Intelligence Comm., vol. 16, no. 2,pp.71-87.
  7. Jay B. Simha, Iyenger S.S. 2006. Fuzzy data mining for customer loyalty analysis. In Proc. Of 9th Intl. Conf. (IEEE) on Information Technology.
  8. Qiaohong Z., Wenfeng L. 2008. The Research of Customer Classification Based on Extended Bayes Model. Pervasive Computing And Applications, ICPCA, Third International Conference, Vol.1,IEEE.
  9. http://www.cec.uchile.cl/~his02/index_files/rough_tutorial.pdf
  10. Lopez J., Aguado J. et al. 2011. Hopfield K-means clustering algorithm: A proposal for the segmentation of electricity customer. Electric power system research.
  11. Wan I-Lee, Bih-Yaw S. 2009. Application of neural network to recognize profitable customers for dental services marketing. Expert system with applications.
  12. Shouhong W. 2005. Classification with incomplete survey data- A Hopfield neural network approach. Computers and Operations research.
  13. Guo B., Wang D., Yan S., Zhong L. 2006. Hardware-Software partitioning of real-time operating systems using Hopfield neural networks. Neurocomputing.
  14. Han J., Kamber M. 2006. Data Mining: Concepts and Techniques. Morgan Kauffmann Publishers.
  15. Kumar V., Pang-Ning T., Michael S. 2006. Introduction to Data Mining. Pearson Education.
  16. Peng Y., Flach P. 2001. Soft Discretization to Enhance the Continuous Decision Tree Induction. Integrating Aspects of Data Mining, Decision Support and Meta-Learning, Christophe Giraud-Carrier, Nada Lavrac and Steve Moyle, editors, pages 109–118, ECML/PKDD’01 workshop notes.
  17. Cristina O., Louis W., 2003. A Complete fuzzy decision tree technique. Intl. J. of Fuzzy sets and systems, Elsevier.
  18. Andreas M., Nicolas W., Martin A., Miltiadis S. 2005. Using a Fuzzy classification query language for customer relationship management. In Proc. Of Intl. Conf. of VLDB, Norway.
  19. Yu-Zhe Chen, Ming-Hua Zhao, Shu-Liang Zhao, Yan W. 2006. A customer intelligent system based on improving LTV model and data mining. In Proc. 5th Intl. Conf. On Machine Leaning and Cybernetics, Dalian.
  20. Daniel So Y., Juan S., Xi-Zhao W. 2002. An initial comparison of generalization-capability between crisp and fuzzy decision trees. In Proc. Of Intl. Conf. on Machine Learning and Cybernetics, Beijing.
  21. Yen J., Langari R. 1998. Fuzzy logic: Intelligence, Control and Information, Prentice-Hall, Inc.
  22. Ue-Pyng W., Kuen-Ming L., Hsu-Shih S. 2009. A review of Hopfield Neural networks for solving mathematical programming problems. Elsevier.
  23. http://www.comp.leeds.ac.uk/ai23/reading/Hopfield.pdf.
  24. Yan-Li L., Jing-Yuan H., Fa-Chao L.,Shu-Shan L. 2005. Fuzzy synthetic evaluation on customer loyalty based on analytic hierarchy process. In Proc. Of 4th Intl. Conf. on Machine learning and Cybernetics.
  25. Yue W. et al. 2012. Storage capacity of the Hopfield network associative memory. Fifth international conference on Intelligent Coomputation technology and automation.
Index Terms

Computer Science
Information Sciences

Keywords

Classification Customer Loyalty K-medoid clustering Hopfield Neural Network Normalization Matrix similarity Euclidean Norm