This algorithm is an improvement to the Apriori method. A frequent pattern is generated without the need for candidate generation. FP growth algorithm represents the database in the form of a tree called a frequent pattern tree or FP tree.
This tree structure will maintain the association between the itemsets. The database is fragmented using one frequent item. This fragmented part is called “pattern fragment”. The itemsets of these fragmented patterns are analyzed. Thus with this method, the search for frequent itemsets is reduced comparatively.
In here we have apply FP growth algorithm on cosmetic data set which was extracted from an online store. https://www.kaggle.com/mkechinov/ecommerce-events-history-in-cosmetics-shop
We have selected only the purchase event_type and try to find association rules between user purchase brands. Applied minSup = 0.01 and minCon = 0.01
Result Interpretation 😊 Final output consists of several association rules and one special feature was highlighted. In most of the rules brand Domix and brand Pole were included. The predicted brands in most of the rules consisted of brand Domix and brand Pole. So it can be concluded that customers have more of a trend on buying products from both Domix and Pole brands together.