期刊名称：International Journal of Computer Science & Technology
印刷版ISSN：2229-4333
电子版ISSN：0976-8491
出版年度：2012
卷号：3
期号：4
页码：102-104
语种：English
出版社：Ayushmaan Technologies
摘要：Clustering plays an important role in providing intuitive navigation and browsing mechanisms by organizing large amounts of information into a small number of meaningful clusters The main concept is similarities/dissimilarities measure from multiple viewpoints. In this paper, we propose a Multi-Viewpoint based Similarity measuring method, named MVS. MVS is potentially more suitable for text documents than the popular cosine similarity. MVS, two criterion functions, IR and IV, and their respective clustering algorithms, MVSC-IR and MVSC-IV, have been introduced. Compared with other state-of-the-art clustering methods that use different types of similarity measure, on a large number of document datasets and under different evaluation metrics, the proposed algorithms show that they could provide significantly improved clustering performance.
