期刊名称：International Journal of Computer Trends and Technology
电子版ISSN：2231-2803
出版年度：2015
卷号：21
期号：1
页码：34-40
DOI：10.14445/22312803/IJCTT-V21P107
出版社：Seventh Sense Research Group
摘要：Clustering is the one of the major important task in data mining .The task of clustering is to find the fundamental structures in data and categorize them into meaningful subgroups for supplementary study and examination. Existing KMeans clustering with MVS measure it doesn't best position to cluster the data points. This problem will lead to gain less optimal solution for clustering method. Using multiple viewpoints, more informative assessment of similarity could be achieved. Theoretical analysis and empirical study are conducted to support this claim. Two criterion functions for document clustering are proposed based on this new measure. We compare them with several wellknown clustering algorithms that use other popular similarity measures on various document collections to verify the advantages of our proposal. In this proposed approach, multiview clustering is applied on different applications namely on text documents and realtime document clustering on local disks. Proposed approach gives better clustering accuracy in terms of different sizes of data.
关键词：K-Means clustering with MVS measure itdoesn't best position to cluster the data points.
