Search for Patterns Applying Association Rules in the Retail Sector using Orange

Abstract

The retail companies are in a constant struggle to maintain and raise their profits as well as to offer the services and products the clients wish to acquire. In order to achieve this, they are in an ongoing search for strategies for decision making that produce positive values for the business. To reach this goal, and to get clear and efficient strategies, possessing a large amount of data gathered on the commercial transactions, the need to analyze in an intelligent way the aforementioned data arises, extracting useful knowledge to support the decision making and an understanding of the association patterns that take place in the sales-clients behavior. This research has the objective of execute the search for patterns applying data mining techniques to find new and better Association Rules on the database of a retail company, using the data provided by them and the Data Mining tool: Orange Canvas employing the FP-Growth algorithm.