Faculty of engineering students' success analysis with clustering methods

In this study, data clustering analysis for the student of faculty of engineering carried out. Cluster analysis, using the different characteristics or similar properties of objects in the data set, aims at creating in the same cluster homogeneous and between different clusters heterogeneous groups. This is the process of analyzing students' demographic data, and settlement in University Entrance Exam scores success percentages weighted grade point average information gained will be used. In addition, examining the general characteristics of the clusters formed and the regions and school types of the students have interpreted. Hard and fuzzy clustering algorithms are used in study and their performances are compared. Outlier detection was performed for the clusters with Box-Plot analysis which used as a tool to measure the success of the methods in the study.