COMPARISON OF MARKET BASKET ANALYSIS METHOD USING APRIORI ALGORITHM, FREQUENT PATTERN GROWTH (FP- GROWTH) AND EQUIVALENCE CLASS TRANSFORMATION (ECLAT) (CASE STUDY: SUPERMARKET “X” TRANSACTION DATA FOR 2021)
##plugins.themes.bootstrap3.article.main##
Abstract
The retail industry continues to grow and develop in Indonesia. The retail sector as a provider of goods used in everyday life has long started digital transformation in its business. Digital technology helps the retail industry collect valuable customer data. Business analytic is the use of data, information technology and statistical analysis, which is used to obtain information about a business and make decisions based on facts. Business analytic turns data into steps or actions in the context of making business decisions. Consumer needs and purchasing behavior can be predicted with big data-based technology. Association Rule is a technique in data mining to find the relationship between items in an item set combination. One of the utilizations of the association rule method is market basket analysis. Algorithms that can be used to analyze consumer purchasing patterns include the Apriori algorithm, Frequent Pattern Growth (FP-Growth) which represents a database structure in a horizontal format, and the Equivalence Class Transformation (ECLAT) algorithm which represents a vertical data format. In addition, this research will first analyze the complexity of the algorithm based on the time complexity in running the algorithm. This analysis uses these three algorithms, which are applied to Supermarket "X" transaction data in 2021, namely 136,202 transactions. The measure of goodness that is used to find out the best algorithm uses support and confidence values. The results show that the ECLAT algorithm is the most superior algorithm compared to the others based on the execution time required by the algorithm. The support value used in forming associations in the ECLAT algorithm is 1%, resulting in 19 rules. From the results of these rules, the highest support value was generated by the purchase of Indomie goreng special and Indomie ayam bawang, where as many as 1,362 shopping transactions bought these two items together or 2.71% of the total transactions.
Downloads
##plugins.themes.bootstrap3.article.details##
Copyright (c) 2023 Rina Wahyuningsih, Agus Suharsono, Nur Iriawan
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
Blattberg, RC, Do Kim, B., and Neslin, SA (2008), Database Marketing: Analyzing and Managing Customers, Springer Science+Business Media, New York
Cios, KJ, Pedrycz, W., Roman WS, and Lukasz AK (2007), Data Mining: A Knowledge Discovery Approach, Springer Science+Business Media, New York.
Evans, J. (2017), Business Analytics. Pearson Education Limited, England
Gayathri, GS (2017), “Performance Comparison of Apriori, ECLAT and FP-Growth Algorithm for Association Rule Learning”, International Journal of Computer Science and Mobile Computing, Vol. 6 Issue 2, p. 81-89
Gunadi, G. and Dana, IS (2012), "The Application of the Data Mining Market Basket Analysis Method to Book Product Sales Data Using the Apriori Algorithm and Frequent Pattern Growth (FP-Growth): Case Study of Printing PT. Gramedia”, MKOM Telematics Journal, Vol. 4 No. 1, pp. 118-132
Han, J., Micheline, K., and Pei, J. (2012), Data Mining Concepts and Techniques 3rd edition, Elsevier Inc., USA
Heaton, J. (2016), Comparing dataset characteristics that favor the Apriori, ECLAT or FP-Growth frequent itemset mining algorithms. In Southeast Con 2016 (pp. 1-7). IEEE.
Larose, DT and Larose, CD (2014), Discovering Knowledge in Data.2nd edition, John Wiley and Sons, Inc., New Jersey
Ma, Z., Juncheng, Y., Taixia Z., and Fan, L (2016), “An Improved ECLAT Algorithm form Mining Association Rules Based on Increased Search Strategy”,InternationalJournal of Database Theory and Application,Vol. 9 No. 5, pp. 251-266
Olson, D. L and Delen, D. (2008), Advanced Data Mining Techniques, Springer, Heidelberg
Ong, JO, Sutawijaya, AH, and Ahmad, BS (2020), "Innovation Strategy for Modern Retail Business Models in the Industrial Age 4.0", Scientific Journal of Business Management, Vol. 6, No. 02
Pujianto, A., Awin, M., and Rachmawati, N. (2018), Utilization of Big Data and Consumer Privacy Protection in the Digital Economy Era, Vol. 15 No. 2, pp. 127-137
Sharma, A. and Anita, G. (2021), “Association Rule Mining Algorithm: A Comparative Review”, International Research Journal of Engineering and Technology (IRJET), Vol. 03 issue 11, p. 848-853
Soliha, E. (2008), Analysis of the Retail Industry in Indonesia, Journal of Business and Economics, Vol. 15, No. 2, pp. 128-142