Akademik Veri Yönetim Sistemi
Düzce Üniversitesi Bilim ve Teknoloji Dergisi, cilt.7, sa.3, ss.1176-1186, 2019 (Hakemli Dergi)
The extracting association rules of inter-user-product relations used by companies in decision-making processeshave been popular for some time, especially for market basket analysis. In this study it is aimed to discoverassociation rules from original online store transaction of a Turkish retail company, in order to help administratorand decision maker also Customer Relationship Management department to initiate campaigns. The mainobjective is to find out which product item sets are bought together. In order to better compare the results thedata are analyzed with and without clustering according to range of ages and gender. Data mining Associationanalysis methods such as Apriori Algorithm, FP-Growth (Frequent Pattern) then applied which are used toextract association rules. Moreover some of the collaborative filtering metrics namely Jaccard, Pearson, andCosine function are used to understand the association between products to obtain a recommendation system.The proposed recommendation methods successfully recommended the associated product for the obtainedoriginal dataset as high as %65 accuracy. Obtained association rules are shared with the marketing department toinitiate and direct forthcoming marketing campaigns.