Using data mining techniques to extract profitability strategy

Today, with the development of base systems and the high volume of data stored in them, there is a need for tools to process and use these data. The best tool is to discover rules between data, data mining and data mining techniques. In this article, we are looking for data mining tools to extract the profitability rules. In order to obtain the tangible results of this research, the information of customers of a restaurant in two parts has been collected through the distribution of a questionnaire. The first part is customer personal information, and the second part is the importance of each of the loyalty factors assigned by each customer. After collecting the data, the Excel file was imported and analyzed by Rapid Miner software, and clustering was performed on all customer information and customers were placed in clusters based on similarity of features and their behavior. Clustering is performed using the K-means algorithm. Then, an index is defined for categorization and is categorized on each cluster of clustering, and customers of each cluster are divided into two groups of profitable and non-profitable, and after extracting the rules from the decision tree, the strategies are presented for making more profit in the studied location.