Implementasi Data Mining Analisa Pola Belanja Customer Dengan menggunakan FP-Growth pada Produk Fashion

Agustin, Ririn
Budiman, Adam Arif

Journal Technology Information and Data Analytic
Vol 1 No 1 (2024): Journal Technology Information and Data Analytic (TIFDA)

This study applies data mining to analyze customer patterns and fashion product predictions. The FP-Growth method is used to identify frequently occurring itemset patterns,The dataset contains customer purchase history and fashion product attributes. The results of customer pattern analysis and fashion product predictions can help fashion companies in making strategic decisions. This study contributes to the use of data mining to understand customer preferences and improve business decisions for fashion companies. The use of datasets consisting of customer purchase history and fashion product attributes. First, using the FP-Growth algorithm, an analysis is carried out to identify frequently occurring itemset patterns in customer data. The results of the analysis are used to understand customer preferences and shopping habits.