Implementation of data mining techniques on knowledge economy variables

The recording of data from the past to the present and the creation of large-scale databases with these records has widened data mining techniques that enable the discovery of confidential information in these data. In this thesis, clustering analysis was conducted by the 35 countries's GDP, unemployment and the import-export ratio variables between the 2013-2016 years. The 35 countries include European Union member states and Turkey. Knowledge Economy Variables which seperates the countries grouped as economic level are analyzed with Decision Trees. As a complementary process, the countries are segmented based on their knowledge economy variables, welfare level, years of EU membership, economic size and geographic locations, by means of and Kohonen Maps, which are remarkable techniques of Clustering. Finally, the application results are presented within this frame .