Performance evaluation of enhanced hierarchical and partitioning based clustering algorithm (EPBCA) in data mining

Clustering is a way of combining data objects or data points into disjoint cluster. The basic concept behind clustering is that the data objects in the same clusters should be related to each other and the data objects belonging to different clusters should differ from each other. This research paper proposes a new algorithm which combines the features of K-means clustering algorithm and Hierarchical clustering algorithm BIRCH. The proposed algorithm first perform hierarchical clustering on the dataset which gives a large number of clusters and then further perform partitioning clustering using K-Means partitioning clustering algorithm to reduce the number of clusters and get more accuracy. The proposed algorithm is applied on cars dataset which is then compared with K-means clustering algorithm. The comparison is done on the basis of within sum square error in which the new algorithm give better results as compare to K-Means clustering algorithms.