Cluster quality based performance evaluation of hierarchical clustering method

Clustering is an important phase in data mining. A number of different clustering methods are used to perform cluster analysis: Partitioning Clustering, hierarchical clustering, grid-based clustering, model-based, graph based clustering and density based clustering and so on. Hierarchical method helps us to cluster the data objects in the form of a tree known as hierarchy. And each node in hierarchy is known as the cluster. Hierarchical clustering can be performed in two ways: agglomerative clustering and divisive clustering. Agglomerative clustering is always more preferable. For a good cluster analysis, the quality of the clusters should be high. In this paper, we will measure the quality of clusters with the help of three parameters: Cohesion measurement, Silhouette index and Elapsed time.