Hierarchical Clustering with Multiviewpoint Based Similarity Measure for document Clustering

A cluster is a group of similar objects placed together and are dissimilar to other cluster objects. In this paper, we introduce Hierarchical Clustering with Multiple view points based on different similarity measures. The major difference between a traditional dissimilarity and similarity measure is that the former uses only a single viewpoint, which is the origin, while the latter utilizes many different viewpoints, which are objects assumed to not be in the same cluster with the two objects being measured. The main objective is to cluster web documents. Using Hierarchical Multiview point, we can achieve more informative assessment of similarity. We compare our approach with former model on various document collections to verify the advantages of our proposed method.