Soft Clustering Technique on Academics Performance Evaluation

Clustering techniques are unsupervised learning methods of mining complex and multi-dimensional data sets such that observations in the same cluster are similar in some sense. The student academic performance evaluation problem can be considered as a clustering problem where clusters are formed on the basis of students intelligence. Choosing the right clustering technique for a given dataset is a research challenge. Therefore, intelligence-based grouping is essential for maintaining the homogeneity of the group; otherwise it would be difficult to provide good educational recommendation to the highly diverse student population. Homogenous grouping of students with similar result ranking into classes would further make student academic performance analysis detailed and sufficient for recommendation. Grouping of students using Fuzzy C-Means (FCM) techniques with the level of their degree of membership into different clusters allows for overlapping of boundaries and resolve sharp boundary problems as opposed to crisp-based method. FCM technique will reveal the degree of membership trend in the clusters which is the focus of this work. In this work, we implemented Soft clustering technique (Fuzzy CMeans) in C++ for student academic performance analysis. This will proffer recommendations that will enhance student performance