Market basket analysis by using association rule mining algorithm

The mass of products produced by the product variety in the stores causes intensities during the goods entry and exit in the store depots. It is thought that this intensity affects labor, time, energy consumption and sales negatively. Data mining works produce solutions to identify demands in sectors, to find and develop the most suitable solution to the demands. Association rule analysis algorithms are one of the most frequently used methods to reveal the relationships between data sets or data in the field of data mining. In the light of the data obtained from the literature review, it was found that the most commonly used algorithms in association rule analysis algorithms are Apriori and FP-Growth algorithms. In this thesis, 4625 mobility and 106 products belonging to a store warehouse, products with a tendency to coexist from the warehouse by using the rule extraction algorithms. Apriori and FP-Growth algorithms are examined comparatively on the application and it is intended to optimize the store warehouse for entry-exit. This thesis consists of a total of five chapters: introduction, general information about data mining, detailed information about the association rule analysis, the application section and the result.