Document Retrieval using Hierarchical Agglomerative Clustering with Multi-view point Similarity Measure Based on Correlation: Performance Analysis

Clustering is one of the most interesting and important tool for research in data mining and other disciplines. The aim of clustering is to find the relationship among the data objects, and classify them into meaningful subgroups. The effectiveness of clustering algorithms depends on the appropriateness of the similarity measure between the data in which the similarity can be computed. This paper focus on performance analysis of Agglomerative clustering with Multi Viewpoint based on Cosine similarity and Correlation similarities for finding the relationship between different documents and clustering them. The experiment is conducted over fifteen text documents and the performance of the proposed method is analyzed thoroughly and compared to Hierarchical Agglomerative clustering with Multi Viewpoint that is based on cosine similarity. The experimental results clearly shows that the proposed model Hierarchical Agglomerative clustering with Multi Viewpoint, based on correlation similarity perform quite well for document retrieval.