Agglomerative Hierarchical Clustering (AHC) Method for Data Mining Sales Product Clustering
-
Ridha Maya Faza Lubis
*
Southern Taiwan University of Science and Technology,
Taiwan, Province of China
- Jen-Peng Huang Southern Taiwan University of Science and Technology, Taiwan, Province of China
- Pai-Chou Wang Southern Taiwan University of Science and Technology, Taiwan, Province of China
- Kiki Khoifin Southern Taiwan University of Science and Technology, Taiwan, Province of China
- Yuli Elvina Southern Taiwan University of Science and Technology, Taiwan, Province of China
- Dyah Ayu Kusumaningtyas Southern Taiwan University of Science and Technology, Taiwan, Province of China (*) Corresponding Author
Abstract
Supermarkets are Indonesian terms that refer to large stores or supermarkets that offer a variety of daily needs such as food, drinks, cleaning products, household appliances, clothing, and so on. In contrast to stalls or small shops, supermarkets have a larger size and provide a variety of products. Because of this, many people prefer to shop for their daily needs at the supermarket rather than at the nearest shop because the existence of the supermarket makes it easier for consumers to buy various products in one place without having to move to another store. However, sales in supermarkets also pose a problem, namely how to sort or group products that are not selling well so they can be replaced with products that are selling better or reduce the number of suppliers. This is where data mining or data analysis techniques that use business intelligence are needed. The research was conducted to classify the best-selling products in supermarkets using the Agglomerative Hierarchical Clustering (AHC) method, in which alternatives with the same matrix or distance are grouped into certain clusters. In applying the AHC method, the number of clusters formed is 3. There are three different clusters, namely cluster 0, cluster 1, and cluster 2, each with a different alternative group. Each cluster has a different number of products and a different percentage. Cluster 0 is the cluster with the highest number of products and the largest percentage, namely 45% with a total of 9 products, followed by cluster 2, and cluster 1 has the smallest number of products and percentage, namely 0.30% with a total of 6 products and 0 .25% with a total of 5 products. In addition, sales data for several products each month are grouped based on certain price ranges
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Copyright (c) 2023 Ridha Maya Faza Lubis, Jen-Peng Huang, Pai-Chou Wang, Kiki Khoifin, Yuli Elvina, Dyah Ayu Kusumaningtyas
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