Published Paper Details:

There is a fast-growing large amount of available electronic information such as online newspapers, journals, conference proceedings, Web sites, e-mails, etc. Using all this electronic information controlling, indexing or searching is not possible for human and for search engines also. Thus, automatic document organization is a critical issue. By using document clustering methods, we can understand data distribution or we can pre-process data for other applications. For an example, if a search engine uses clustered documents to search an item or data, it can produce results more effectively and efficiently. Document clustering is an approach of automatic clustering operation of text documents so that related documents are presented in same cluster, unrelated documents are presented in different clusters. Document Clustering is a technique of an unsupervised learning to group related documents into a cluster, each cluster consist of documents that are related to one another within the same clusters and are unrelated to documents belonging to other cluster. Before applying any clustering methods, a similarity (related)/distance measures must be determined. The similarity(related) measure reflects the degree of closeness of the target objects. In this paper, we introduce document clustering on Multiview point based similarity measure and two related clustering methods. The existing traditional dissimilarity/similarity based document clustering measure uses only a single viewpoint. This is the origin, while ours usesmany other different viewpoints.