ADS-B system for air traffic managementevaluation with efficiency methods

In our ever-changing and developing world, evaluating a large amount of data and converting it into information requires a long process and process. Thanks to changing and developing market conditions and renewed technologies, the way data is gathered and stored has gained very different dimensions. Companies want to process data in aggregate form in digital environment and use it to make future analysis and forecasts. Data that is not transformed into information is not very valuable for companies. Therefore, data mining and machine learning methods come into play in the process of converting data into meaningful information. Data mining implements various processes / methods / methods in order to give meaning and value to data and enables its transformation into information. The transformed data are used to predict the future in the fields of sales, marketing, insurance, banking and computer science. Within the scope of this thesis, classification and clustering operations will be performed on an ADS-B flight data sets with the WEKA and ORANGE program, which is a free software tool used in data mining. The security and end-to-end monitoring of these systems has become more complex, given the increasing number of airline transport and the ever-increasing flights. The ADS-B system, which is a new generation air traffic management (nextgen) system that produces a new solution to these problems and eliminates the weaknesses of conventional land-based radars, will be examined in detail. With the investigation, valuable information that the flight data contains using data mining methods will be revealed. In this way, the performance comparison of the classification and clustering algorithms presented for our data set will be made. In addition, data mining platforms in use will be compared and examined in many aspects, especially their features such as the number of algorithms, classification, clustering, etc., according to their paid / unpaid status.