International Journal of Science and Modern Engineering (IJISME)
Educational data mining is current growing research area and the main essence of data mining concepts are used in the educational field for extracting useful information of the students based on their behavior in the learning process. Prior approaches used decision tree classifications optimized with ID3 algorithms to obtain such patterns but discovering the implicative tendencies is valuable information for the decision-maker which is absent in tree based classifications. So the authors propose to use outlier detection for mining and evaluating educational data of students. In this paper, outlier detection mechanisms are used for identifying outliers which improve the quality of decision making.