Comparative Research of Clustering Algorithms for Prediction of Academic Performance of Students

Clustering is a task of assigning a set of objects into groups called clusters. In general the clustering algorithms can be classified into two categories. One is hard clustering; another one is soft (fuzzy) clustering. Hard clustering, the data’s are divided into distinct clusters, where each data element belongs to exactly one cluster. In soft clustering, data elements belong to more than one cluster, and associated with each element is a set of membership levels In order to monitor the progress of students efficiently, different clustering algorithms are applied to the academic results of students so as to categorize in appropriate class as per their performance. We proposed the use of FCM and KFCM clustering algorithms for prediction of students’ academic performance. Euclidean distance as a measure of similarity measurement is taken into consideration. These algorithms are applied and performance is evaluated on the basis of clustering output. FCM allows data points to belong to more than one cluster where each data point has a degree of membership of belonging to each cluster. The KFCM whereas uses a mapping function and gives better performance as compared to FCM. The summarized result shows that KFCM gives better performance than FCM Keywords—FCM, KFCM, clustering, academic performance, membership function