Study and Analysis of Multi - viewpoint clustering with similarity measures

Abstract The database object that describes tens of attributes is referred as high dimensional data space. In high dimensional data, the common distance measures can be influenced by noise. Existing clustering algorithms are implemented based on partitioning, hierarchical, density based and grid based. These methods assume some kind of cluster relationship among the clustered objects. Similarity among the pair of objects may be defined as implicitly or explicitly. Our main objective is to cluster web documents. So, in this paper we propose “multi viewpoint based clustering methods with similarity measure” approach for clustering high dimensional data. This approach makes use of different viewpoints from different objects of multiple clusters and more useful assessment of similarity could be achieved. Analysis and experimental study are conducted in support of this approach.