Outlier Finding from Clusters using Categorical Data

Abstract – A New Categorical Data algorithm which combines a systematic method for finding initial centroids and an efficient way for assigning data points to clusters. This method ensures the entire process generate cluster without sacrificing the accuracy. It always generate good cluster by reducing the mean square error. Our clustering algorithm serves as a good benchmark to monitor the progression of student’s performance in institute. It also enhances the decision making by academic planners to monitor the student’s performance semester by semester by improving on the future academic results in the subsequence academic session.