Market Basket Analysis to Identify Customer Behaviours by Way of Transaction Data

Fachrul Kurniawan, Binti Umayah, Jihad Hammad, Supeno Mardi Susiki Nugroho, Mochammad Hariadi

Abstract


Transaction data is a set of recording data result in connections with sales-purchase activities at a particular company. In these recent years, transaction data have been prevalently used as research objects in means of discovering new information. One of the possible attempts is to design an application that can be used to analyze the existing transaction data. That application has the quality of market basket analysis. In addition, the application is designed to be desktop-based whose components are able to process as well as re-log the existing transaction data. The used method in designing this application is by way of following the existing steps on data mining technique. The trial result showed that the development and the implementation of market basket analysis application through association rule method using apriori algorithm could work well. With the means of confidence value of 46.69% and support value of 1.78%, and the amount of the generated rule was 30 rules.


Full Text:

PDF

References


M. Kaur and S. Kang, “Market Basket Analysis: Identify the Changing Trends of Market Data Using Association Rule Mining,” Procedia Comput. Sci., vol. 85, no. Cms, pp. 78–85, 2016. crossref

A. Mansur and T. Kuncoro, “Product Inventory Predictions at Small Medium Enterprise Using Market Basket Analysis Approach-Neural Networks,” Procedia Econ. Financ., vol. 4, no. Icsmed, pp. 312–320, 2012. crossref

X. Su, “Intertemporal Pricing with Strategic Customer Behavior,” Manage. Sci., vol. 53, no. 5, pp. 726–741, 2007. crossref

G. Armstrong, S. Adam, S. Denize, and P. Kotler, Armstrong, G., Adam, S., Denize, S., & Kotler, P. Pearson Australia., 2014.

E. Sherman, A. Mathur, and R. B. Smith, “Store Environment and Consumer Purchase Behavior: Mediating Role of Consumer Emotions,” Psychol. Mark., vol. 14, no. 4, pp. 361–378, 1997. crossref

N. Jothi, N. A. Rashid, and W. Husain, “Data Mining in Healthcare - A Review,” Procedia Comput. Sci., vol. 72, pp. 306–313, 2015. crossref

A. Bertoni and T. Larsson, “ScienceDirect Data Mining in Product Service Systems Design: Literature Review and Research Questions,” Procedia CIRP, vol. 64, pp. 306–311, 2017. crossref




DOI: http://dx.doi.org/10.17977/um018v1i12018p20-25

Refbacks

  • There are currently no refbacks.


Copyright (c) 2017 Knowledge Engineering and Data Science

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Flag Counter

Creative Commons License


This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

View My Stats