Open AccessImplementation of K-Medoids and FP-Growth Algorithms for Grouping and Product Offering Recommendations
Author(s) -
Imaduddin Syukra,
Assad Hidayat,
Muhammad Zakiy Fauzi
Publication year - 2019
Publication title -
indonesian journal of artificial intelligence and data mining
Language(s) - English
Resource type - Journals
eISSN - 2614-6150
pISSN - 2614-3372
DOI - 10.24014/ijaidm.v2i2.8326
Subject(s) - database transaction , association rule learning , medoid , product (mathematics) , transaction data , sales promotion , cluster analysis , computer science , value (mathematics) , data mining , marketing , business , database , mathematics , sales management , machine learning , geometry
212 Mart Rambutan Street on Pekanbaru City is a company engaged in retail. Meeting the needs of consumers and making the right decision in determining the sales strategy is a must. One way to find out market conditions is to observe sales transaction data using data mining. The data mining method commonly used to analyze market basket (Market Basket Analysis) is the Association Rule. The Association Rule can provide product recommendations and promotions, so that the marketing strategy is more targeted and the items promoted are the customer's needs. At 212 Mart, the determination of product promotion is obtained from the analysis of sales transaction data reports, which are based on the most sold products and the expiration date. Often the product being promoted does not fit the customer's needs. The purpose of this study is to apply the K-Medoids algorithm for clustering on FP-Growth in producing product recommendation rules on a large number of datasets so that they can provide technical recommendations / new ways to the 212 Mart in determining product promotions. The results obtained are from the experiments the number of clusters 3 to 9 obtained optimal clusters of 3 clusters based on the validity test of the Davies Bouldin Index with a value of 0.678. With a minimum support value of 5% - 9% and a minimum value of 50% confidence, the result is that the Association Rule is found only in cluster 3 with 5 rules.