Implementation of Hierarchical Clustering with Multiviewpoint-Based Similarity Measure?

Clustering is one of the most important data mining or text mining algorithm that is used to group similar objects together. The aim of clustering is to find the relationship among the data objects, and classify them into meaningful subgroups. The effectiveness of clustering algorithms depends on the correctness of the similarity measure between the data in which the similarity can be computed. This paper focus on implementation of Agglomerative hierarchical clustering with Multiviewpoint based similarity measure for effective document clustering. The experiment is conducted over sixteen text documents and performance of the proposed model is analysed and compared to existing standard clustering method with MVS. The experiment results clearly shows that the proposed model Hierarchical Agglomerative Clustering with Multiview Point based Similarity Measure perform quite well.