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http://dx.doi.org/10.36498/kbigdt.2022.7.1.213

Association Analysis of Product Sales using Sequential Layer Filtering  

Sun-Ho Bang (인천대학교 동북아물류대학원)
Kang-Hyun Lee (인천대학교 동북아물류대학원)
Ji-Young Jang (인천대학교 동북아물류대학원)
Tsatsral Telmentugs (인천대학교 동북아물류대학원)
Kwnag-Sup Shin (인천대학교 동북아물류대학원)
Publication Information
The Journal of Bigdata / v.7, no.1, 2022 , pp. 213-224 More about this Journal
Abstract
In logistics and distribution, Market Basket Analysis (MBA) is used as an important means to analyze the correlation between major sales products and to increase internal operational efficiency. In particular, the results of market basket analysis are used as important reference data for decision-making processes such as product purchase prediction, product recommendation, and product display structure in stores. With the recent development of e-commerce, the number of items handled by a single distribution and logistics company has rapidly increased, And the existing analytical methods such as Apriori and FP-Growth have slowed down due to the exponential increase in the amount of calculation and applied to actual business. There is a limit to examining important association rules to overcome this limitation, In this study, at the Main-Category level, which is the highest classification system of products, the utility item set mining technique that can consider the sales volume of products together was used to first select a group of products mainly sold together. Then, at the sub-category level, the types of products sold together were identified using FP-Growth. By using this sequential layer filtering technique, it may be possible to reduce the unnecessary calculations and to find practically usable rules for enhancing the effectiveness and profitability.
Keywords
Market Basket Analysis; High Utility Itemset Mining; Distribution and Logistics;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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