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http://dx.doi.org/10.6109/jicce.2018.16.1.52

Odoo Data Mining Module Using Market Basket Analysis  

Yulia, Yulia (Department of Informatics, Petra Christian University)
Budhi, Gregorius Satia (Department of Informatics, Petra Christian University)
Hendratha, Stefani Natalia (Department of Informatics, Petra Christian University)
Publication Information
Journal of information and communication convergence engineering / v.16, no.1, 2018 , pp. 52-59 More about this Journal
Abstract
Odoo is an enterprise resource planning information system providing modules to support the basic business function in companies. This research will look into the development of an additional module at Odoo. This module is a data mining module using Market Basket Analysis (MBA) using FP-Growth algorithm in managing OLTP of sales transaction to be useful information for users to improve the analysis of company business strategy. The FP-Growth algorithm used in the application was able to produce multidimensional association rules. The company will know more about their sales and customers' buying habits. Performing sales trend analysis will give a valuable insight into the inner-workings of the business. The testing of the module is using the data from X Supermarket. The final result of this module is generated from a data mining process in the form of association rule. The rule is presented in narrative and graphical form to be understood easier.
Keywords
Data mining; FP-Growth; Market Basket Analysis; Odoo;
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  • Reference
1 L. W. Santoso and Yulia, "Data warehouse and big data technology for higher education," Procedia Computer Science, vol. 124, pp. 93-99, 2017. DOI: 10.1016/j.procs.2017.12.134.   DOI
2 G. Moss, Working with openERP. Brimingham: Packt Publishing, 2013.
3 D. Reis, Odoo Development Essentials. Brimingham: Packt Publishing, 2015.
4 S. Riggs and H. Krossing, PostgreSQL 9 Administration Cookbook. Brimingham, UK: Packt Publishing, 2010.
5 M. Narvekar and S. F. Syed, "An optimized algorithm for association rule mining using FP tree," Procedia Computer Science, vol. 45, pp. 101-110, 2015. DOI: 10.1016/j.procs.2015.03.097.   DOI
6 D. T. Larose, Discovering Knowledge in Data: an Introduction to Data Mining. Hoboken, NJ: John Wiley & Sons, 2005.
7 J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3rd ed. San Fransisco, CA: Morgan Kaufman, 2012.
8 X. D. Wu and V. Kumar, The Top Ten Algorithms in Data Mining. Boca Raton, FL: CRC Press, 2009.
9 P. T. Tan, M. Steinbach and V. Kumar, Introduction to Data Mining. Boston, MA: Pearson, 2005.
10 J. Li and D. T. Ming, "Research of an association rule mining algorithm based on FP tree," in Proceedings of IEEE International Conference on Intelligent Computing and Intelligent Systems, Xiamen, China, pp. 559-563, 2010. DOI: 10.1109/ICICISYS.2010.5658443.
11 Z. Zheng, R. Kohavi, and L. Mason, "Real world performance of association rule algorithms," in Proceeding of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, pp. 401-405, 2000. DOI: 10.1145/502512.502572.
12 J. Han, J. Pei, Y. Yin, and R. Mao, "Mining frequent patterns without candidate generation: a frequent-pattern tree approach," Data Mining and Knowledge Discovery, vol. 8, no. 1, pp. 53-87, 2004. DOI: 10.1023/B:DAMI.0000005258.31418.83.   DOI