Exploring an Educational System’s Data through Fuzzy Cluster Analysis

Clustering is a very useful technique which helps to enrich the semantics of the data by revealing patterns in large collections of poly-dimensional data. Moreover the fuzzy approach in clustering provides flexibility and enhanced modeling capability, as the results are expressed in soft clusters, allowing partial memberships of data points in the clusters. During the last decade, the digitalization of detailed student records of the University of Elbasan has not only simplified the typical university procedures but also it has created the possibility of a deeper view of the students’ data. The cluster analysis applied on these student data can discover patterns which would assist in several strategic issues like: optimizing the student advising process, organization of curricula, adjusting the compulsory/elective courses, preparing better teaching approaches etc. In our study, besides the classical fuzzy c-means, we will utilize several other variations like the possibilistic fuzzy c-means, the Gustafson-Kessel algorithm and the kernel based fuzzy clustering. We have found the application of several variations of the fuzzy clustering algorithms on these data to be a productive approach. Particular applications sometimes provide useful viewpoints which trigger innovative ideas for the policy-makers of the university.