Customisable Bundling Approach for Online Supermarkets using Association Rules of Product Categories

Authors

  • Vani V Nair Student, Department of Computer Science, East West Institute of Technology, Bengaluru, India
  • Vedashree V Student, Department of Computer Science, East West Institute of Technology, Bengaluru, India
  • Vimala BK Student, Department of Computer Science, East West Institute of Technology, Bengaluru, India
  • Chetana Srinivas Student, Department of Computer Science, East West Institute of Technology, Bengaluru, India
  • Yamuna M Student, Department of Computer Science, East West Institute of Technology, Bengaluru, India

Keywords:

Bundling,, Associative clustering, Support vector machine, Suggestions

Abstract

This research deals with the identification of customers and their buying behavior patterns. The aim is to sell the products which are least preferred by the customers so as to make a cost-effective sale by using bundling approach. A Customized bundling is a group of resources joined together in a single package that has an associated logical name. A bundle is a collection of products which are sold together for a single price. It is the well organized way to make the customer’s shopping self-satisfied. It is implemented by the integration of associative clustering and Support vector machine (SVM) with java.

References

[1]Customer behavior analysis towards online hopping using data mining.,Muqaddas Gull and Arshi Pervaiz,©2018 IEEE.

[2] Customized Bundle Recommendation by Association Rules of Product Categories for Online Supermarkets.,Yan Fang,Xinyue Xiao,Xiaoyu Wang,Huiqing Lan,©2018.

[3] Yang, T. C., & Lai, H. (2006). Comparison of product bundling strategies on different online shopping behaviors. Electronic Commerce Research and Applications, 5(4), 295–304.

[4] Miguéis, V. L., Camanho, A. S., &Falcão e Cunha, J. (2012). Customer data mining for lifestyle segmentation. Expert Systems with Applications, 39(10), 9359–9366.

[5] Liao, S. H., Chen, Y. J., & Hsieh, H. H. (2011). Mining customer knowledge for direct selling and marketing.Expert Systems with Applications, 38(5), 6059–6069.

[6]Fuloria , S . ( 2011 ) How Advanced Analytics Will Inform and Transform U.S. Retail. CognizantReports, July , http://www.cognizant.com/InsightsWhitepapers/How-Advanced-Analytics-Will-Inform-and-Transform-US-Retail.pdf , accessed January 2012 .

[7] Bain Consulting. China Shoppers Report 2017. Beijing: Bein Consulting, 2017.

[8] Jasin S, Sinha A. An LP-based correlatedrounding scheme for multi-item ecommerce orderfulfillment[J]. Operations Research, 2015, 63(6): 1336-1351.

[9] Karimi-Majd, A.-M., &Fathian, M. (2017). Extracting new ideas from the behavior of social network users. Decision Science Letters, 6(3), 207–220.

[10] Miguéis, V. L., Camanho, A. S., &Falcãoe Cunha, J. (2012). Customer data mining for lifestyle segmentation. Expert Systems with Applications, 39(10), 9359–9366.

[11] Liao, S. H., Chen, Y. J., & Lin, Y. T. (2011). Mining customer knowledge to implement online shopping and home delivery for hypermarkets. Expert Systems with Applications, 38(4), 3982– 3991.

[12] Abouee-Mehrizi H, Berman O, Sharma S (2014) Optimal joint replenishment and proactive transshipment policies in a multiperiod inventory system. Working paper, University of Waterloo, Waterloo, Ontario.

[13] Adelman D (2003) Price-directed replenishment of subsets: Methodology and its application to inventory routing. Manufacturing Service Oper. Management 5(4):348–371.

[14] Adelman D, Nemhauser GL, Padron M, Stubbs R, Pandit R (1999) Allocatingfibers in cable manufacturing. Manufacturing Service Oper.

[15] Management 1(1):21–35. Agatz N, Fleischmann M, van Nunen J (2008) E-fulfillment and multi-channel distribution—A review. Eur. J. Oper. Res. 187(2): 339–356. [16] Alptekino ˘glu A, Tang C (2005) A model for analyzing multichannel distribution systems. Eur. J. Oper. Res. 163(3):802–824.

[17] Amazon.com, Inc. (2012) Form 10-K for 2011. Filing date: February 1, 2012. U.S. Securities and Exchange Commission,Washington,DC.

[18] Cattani K, Gilland W, Heese H, Swaminathan J (2006) Boiling frogs: Pricing strategies for a manufacturer adding a direct channel that competes with the traditional channel. Production Oper. Management 15(1):40–56.

[19] Forrester Research, Inc. (2014b) European online retail forecast: 2013 to 2018. Report, May 29. Forrester Research, Inc., Cambridge, MA.

[20] Gans N, van Ryzin G (1999) Dynamic vehicle dispatching: Optimal heavy traffic performance and practical insights. Oper. Res. 47(5):675–692.

[21] Kaplan A (1969) Stock rationing. Management Sci. 15(5):260–267.

[22] Lee H (1987) A multi-echelon inventory model for repairable items with emergency lateral transshipments. Management Sci. 33(10):1302–1316.

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Published

2025-11-26

How to Cite

[1]
V. V. Nair, V. V, V. BK, C. Srinivas, and Y. M, “Customisable Bundling Approach for Online Supermarkets using Association Rules of Product Categories”, Int. J. Comp. Sci. Eng., vol. 7, no. 15, pp. 138–143, Nov. 2025.

Issue

Vol. 7 No. 15 (2019): IJCSE Special Issue May Edition

Section

Research Article

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