Analytical analyses on big data with association rulealgorithms: Airport example

The development of information technology, the expanding the data storage areas, and especially making new studies of cloud computing, enabled to keep the customers' data which belong to companies in bigger sizes. However, with this situation, it became even more difficult to be found out meaningful and useful information from big data. Data mining methods have emerged to solve these needs. Now studies are conducted in many fields with the models and techniques used in the data mining. After these developments, the Association Rules in the field of data mining have gained importance and become a separate research topic. Especially, with the Association Rules, the rules derived from the past data made it easier to be made accurate predictions for the future. In this thesis study, the Association Rules and Algorithms from the data mining models have been examined in detail. Apriori, Apriori-TID, ECLAT, FP-Growth algorithms are used in the application. Furthermore, it has been aimed to perform various analysis and information extraction operation on big data. As an application area, the products sold by food and beverage companies at airports have been selected. Various analysis has been performed with the Association Rules through the big data of these companies which have an heavy passenger traffic. With the information obtained from the analysis, it has been aimed to maximize the profitability by reorganizing the effective campaigns at the sales points, rearranging the products in the department store and the shelves. In addition, it is aimed to obtain different information by the Association Rules, to rearrange the sales strategy of the company bye changing the point of view.