Hierarchical Clustering With Multi-View Point Based Similarity Measure

Clustering is a technique for finding similarity groups in data, called clusters. It groups data instances that are similar to each other in one cluster and data instances that are very different from each other into different clusters. Clustering is often called an unsupervised learning. In this paper Hierarchical clustering is used to find the cluster relationship between data objects in the data set. We introduce a novel multi-viewpoint based similarity measure and two related clustering methods. The main difference of our novel method from the existing one is that it uses only single view point for which it is the base and where as the mentioned clustering with Multi-Viewpoint Based Similarity Measure uses many different viewpoints of objects and are assumed to not be in the same cluster with two objects being measured. Based on this novel method two criterion functions are proposed for document clustering. We compared this clustering algorithm with other measures in order to verify the improvement of novel method.