Call for Paper - January 2023 Edition
IJCA solicits original research papers for the January 2023 Edition. Last date of manuscript submission is December 20, 2022. Read More

Sales Analysis of E-Commerce Websites using Data Mining Techniques

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Anurag Bejju

Anurag Bejju. Article: Sales Analysis of E-Commerce Websites using Data Mining Techniques. International Journal of Computer Applications 133(5):36-40, January 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Anurag Bejju},
	title = {Article: Sales Analysis of E-Commerce Websites using Data Mining Techniques},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {133},
	number = {5},
	pages = {36-40},
	month = {January},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


In the emerging global economy, E-commerce is a strong catalyst for economic development. The rapid growth in usage of Internet and Web-based applications is decreasing operational costs of large enterprises, extending trading opportunities and lowering the financial barriers for active ecommerce participation. Many companies are restructuring their business strategies to attain maximum value in terms of profits as well as customer’s satisfaction. Business tycoons around the globe are realizing that e-commerce is not just trading of products and information over Internet, rather it provides an opportunity to compete with other giants in the market. Data mining (DM) is used to attain knowledge from available information in order to help companies make weighted decisions. An organization needs to invest only on the group of products which are frequently purchased by its customers as well as price them appropriately in order to attain maximum customer satisfaction. The objective of this paper is to evaluate, propose and improve traditional pricing strategies by using web mining techniques to collect information from e- commerce websites and apply data mining methods to induce and extract useful information out of it. The proposed strategy can be generated by optimizing decision trees in an iterative process and exploit information about historical buying behavior of a customer.


  1. Han, Jiawei, Micheline Kamber, and Jian Pei. “Data mining: concepts and techniques” Morgan kaufmann, 2006.
  2. Quinlan J. R. (1986). “Induction of decision trees.Machine Learning,” Vol.1-1, pp. 81-106.
  3. J. R. Quinlan, “C4.5: Programs for Machine Learning,” Morgan Kaufmann Publishers, Inc., 1993.
  4. Ding Xiang-wu and Wang Bin, "An Improved Pre-pruning Algorithm Based on ID3," Jisuanji Yuxiandaihua,Vol.9, pp. 47,2008.
  5. Ming Fan, Xiaofeng Meng translated, “Data mining techniques and concepts”, Machinary Industry Press, Beijing, pp. 136-145, Feb., 2004.
  6. N R Srinivasa Raghavan, ”Data mining in e-commerce: A survey,” Sadhana Vol. 30, Parts 2 & 3, April/June 2005, pp.275–289.
  7. B. Schafer, J.A. Konstan, and J. Reidl, “E-Commerce Recommendation Applications,” Data Mining and Knowledge Discovery, Kluwer Academic, 2001, pp. 115-153.
  8. P. Resnick et al., “GroupLens: An Open Architecture for Collaborative Filtering of Netnews,” Proc. ACM 1994 Conf. Computer Supported Cooperative Work, ACM Press, 1994, pp. 175-186.
  9. J. Breese, D. Heckerman, and C. Kadie, “Empirical Analysis of Predictive Algorithms for Collaborative Filtering,” Proc. 14th Conf. Uncertainty in Artificial Intelligence, Morgan Kaufmann, 1998, pp. 43-52.
  10. Baesens, B. Verstreeten, G. Poel, D. (2004). Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers. European Journal of Operation Research, 156, 508-523.
  11. Cheng, C.H. Chen, Y.S. (2009). Classifying the segmentation of customer value via RFM model and RS theory. Expert System with Applications, 36, 3, 41764184. [12] Hwang, H., Jung, T. Suh, E., (2004). An LTV model and customer segmentation based on customer value


E-Commerce, Data Mining, ID3 Algorithm