Price Changes Analysis Using Association Rule Mining on Online Shopping Portals

  • Authors

    • Aini Atikah Baharudin
    • Sofianita Mutalib
    • Nurzeatul Hamimah Abd Hamid
    • Nor Hayati Abdul Hamid
    2018-12-09
    https://doi.org/10.14419/ijet.v7i4.31.23379
  • Apriori Algorithm, Association Rule Mining, Dynamic Pricing, Tracking.

  • Abstract

    In the recent years, the growth of online marketplace explodes and has become the new norm of shopping. Access to the internet allows consumers to visit local and global online marketplace. By using online shopping, consumers can view the latest products and comparing products' prices. Online marketplace offers flexibility and eases consumers in so many ways. On the contrary, it also has some limitations. Dynamic pricing allows sellers to enhance their marketing strategy by ensuring price competitive with other sellers. The frequent occurrence of real-time price changing limits the user to get the best deal. This paper discusses the analysis of price changes using Association Rule Mining. Price-ChARM finds a frequent pattern of price changes in corresponding to different timelines. On the online marketplace, due to multiple sellers can sell the same item with different offers such as delivery speed and dynamic pricing, the comparison can be quite tricky. We collected data from two well-known portals and we represent the dataset as set of prices for different products for the purpose of frequent itemset mining. We implement the Apriori algorithm and use a total amount of 3,960 records. We generate association rules from the frequent itemsets found in the records and visualize the confidence of 0.9 rules. The association rules represent the pattern of price changes in the portals. This study eases consumer shopping experiences by understanding the trend of price changes to provide a better decision in making a purchase.

     

     

  • References

    1. [1] San, L. Y., Omar, A., & Thurasamy, R. (2015). Online purchase: a study of generation Y in Malaysia. International Journal of Business and Management, 10(6), 298.

      [2] Simanjuntak, H., Sibarani, N., Sinaga, B., & Hutabarat, N. (2015). WEB MINING ON INDONESIA E-COMMERCE SITE: LAZADA AND RAKUTEN. International Journal of Database Management Systems, 7(1), 1.

      [3] Yazdanifard, R., & Li, M. T. H. (2014). The Review of Alibaba’ s Online Business Marketing Strategies which Navigate them to Present Success. Global Journal of Management And Business Research.

      [4] Qing, H. H., & Xue, Z. S. (2009). A model for value-added E-marketplace provisioning: Case study from Alibaba. com. Paper presented at the Conference on e-Business, e-Services and e-Society.

      [5] Harn, A. C. P., Khatibi, A., & Ismail, H. B. (2006). E-Commerce: A study on online shopping in Malaysia. Journal of Social Sciences, 13(3), 231-242.

      [6] Gorodnichenko, Y., & Talavera, O. (2017). Price setting in online markets: Basic facts, international comparisons, and cross-border integration. American Economic Review, 107(1), 249-282.

      [7] Agag, G. M., & El-Masry, A. A. (2017). Why do consumers trust online travel websites? Drivers and outcomes of consumer trust toward online travel websites. Journal of Travel Research, 56(3), 347-369.

      [8] Liebermann, Y., & Stashevsky, S. (2009). Determinants of online shopping: Examination of an early stage online market. Canadian Journal of Administrative Sciences/Revue Canadienne des Sciences de l'Administration, 26(4), 316-331.

      [9] Haque, A., Sadeghzadeh, J., & Khatibi, A. (2006). Identifying potentiality online sales in Malaysia: A study on customer relationships online shopping. Journal of Applied Business Research, 22(4), 119.

      [10] Kooti, F., Lerman, K., Aiello, L. M., Grbovic, M., Djuric, N., & Radosavljevic, V. (2016). Portrait of an online shopper: Understanding and predicting consumer behavior. Paper presented at the Proceedings of the Ninth ACM International Conference on Web Search and Data Mining.

      [11] Pappas, I. O., Kourouthanassis, P. E., Giannakos, M. N., & Lekakos, G. (2017). The interplay of online shopping motivations and experiential factors on personalized e-commerce: A complexity theory approach. Telematics and Informatics, 34(5), 730-742.R. W. Lucky, “Automatic equalization for digital communication,†Bell Syst. Tech. J., vol. 44, no. 4, pp. 547–588.

      [12] Kumar, S., & Rishi, R. (2017). Hybrid Dynamic Price Prediction Model In online Auctions. International Journal of Applied Engineering Research, 12(5), 598-604.

      [13] Lee, S., Illia, A., & Lawson-Body, A. (2011). Perceived price fairness of dynamic pricing. Industrial Management & Data Systems, 111(4), 531-550.

      [14] Aggarwal, C. C., & Yu, P. S. (1998). Mining large itemsets for association rules. IEEE Data Eng. Bull., 21(1), 23-31.

      [15] Géry, M., & Haddad, H. (2003). Evaluation of web usage mining approaches for user's next request prediction. Paper presented at the Proceedings of the 5th ACM international workshop on Web information and data management.

      [16] Cho, Y. H., Kim, J. K., & Kim, S. H. (2002). A personalized recommender system based on web usage mining and decision tree induction. Expert systems with Applications, 23(3), 329-342.

      [17] Kotsiantis, S., & Kanellopoulos, D. (2006). Association rules mining: A recent overview. GESTS International Transactions on Computer Science and Engineering, 32(1), 71-82.

      [18] Song, H. S., kyeong Kim, J., & Kim, S. H. (2001). Mining the change of customer behavior in an internet shopping mall. Expert Systems with Applications, 21(3), 157-168.

      [19] Cai, R., Liu, M., Hu, Y., Melton, B. L., Matheny, M. E., Xu, H., Waitman, L. R. (2017). Identification of adverse drug-drug interactions through causal association rule discovery from spontaneous adverse event reports. Artificial Intelligence in Medicine, 76, 7-15.

      [20] Bernama (2018. E-commerce to lift Malaysia's total trade to RM2 trillion. Retrieved from http://www.bernama.com/en/business/news.php?id=1452913 (10/4/2018)

      [21] Spiliopoulou, M., & Pohle, C. (2001). Data mining for measuring and improving the success of web sites. Data Mining and Knowledge Discovery, 5 (1 - 2), 85 - 114.

      [22] Noor Habibah Arshad, Fauziah Ahmad, Norjansalika Janom, Azlinah Mohamed. (2008). Online transportation services guideline for service quality, WSEAS Transactions on Business and Economics, Volume 5 (5), 201-209.

      [23] Mohd Razif Bin Shamsuddin, Nur Nadhirah Binti Shamsul Sahar, Mohammad Hafidz Bin Rahmat (2017. Eye Detection for Drowsy Driver Using Artificial Neural Network, Conference International Conference on Soft Computing in Data Science, 116-125.

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  • How to Cite

    Atikah Baharudin, A., Mutalib, S., Hamimah Abd Hamid, N., & Hayati Abdul Hamid, N. (2018). Price Changes Analysis Using Association Rule Mining on Online Shopping Portals. International Journal of Engineering & Technology, 7(4.31), 266-271. https://doi.org/10.14419/ijet.v7i4.31.23379

    Received date: 2018-12-07

    Accepted date: 2018-12-07

    Published date: 2018-12-09