抽象的な

Multi View Point Measure for Achieving Highest Intra-Cluster Similarity

Shoban babu Sriramoju

Clustering is one of the data mining techniques which has important utility in real time applications. Cluster is a group of objects with highest similarity. The result of clustering can be used further in many applications including query processing. Clustering can also be used in text mining. The clustering algorithms that are available in this domain uses single viewpoint to find the similarity between object. But, the singe view point similarity measure cannot have highly informative assessment of similarities. In this paper we propose to implement a novel measure known as multiviewpoint based similarity measure. It will consider multiple-viewpoints while measuring similarity which facilitates highly informative assessment of similarity. We built a prototype application to demonstrate the conceptual proof. The empirical results revealed that the measure is effective.