Meta clusters through minimum spanning tree based clustering for performance analysis of students

Abstract

Clustering is a process of discovering group of objects such that the objects of the same group are similar, and objects belonging to different groups are dissimilar. A number of clustering algorithms exist that can solve the problem of clustering, but most of them are very sensitive to their input parameters. Minimum Spanning Tree based clustering algorithm is capable of detecting clusters with irregular boundaries. In this paper we propose Minimum Spanning Tree based clustering algorithm to find Meta clusters (cluster of clusters). The algorithm uses a new cluster validation criterion based on the geometric property of data partition of the data set in order to find the proper number of clusters at each level. The algorithm works in two phases. The first phase of the algorithm create clusters with guaranteed intra-cluster similarity, where as the second phase of the algorithm create dendrogram using the clusters as objects with guaranteed inter-cluster similarity. In this paper we presented experimental result on some synthetic data sets namely student semester mark. Experimental result shows that the proposed algorithm performs better than K-means algorithm. The first phase of the algorithm uses divisive approach, where as the second phase uses agglomerative approach. In this paper we used both the approaches to analyze the performance of the students.