  With the rising quantity of textual data available in electronic format, the
need to organize it become a highly challenging task. In the present paper, we
explore a document organization framework that exploits an intelligent
hierarchical clustering algorithm to generate an index over a set of documents.
The framework has been designed to be scalable and accurate even with large
corpora. The advantage of the proposed algorithm lies in the need for minimal
inputs, with much of the hierarchy attributes being decided in an automated
manner using statistical methods. The use of topic modeling in a pre-processing
stage ensures robustness to a range of variations in the input data. For
experimental work 20-Newsgroups dataset has been used. The F- measure of the
proposed approach has been compared with the traditional K-Means and K-Medoids
clustering algorithms. Test results demonstrate the applicability, efficiency
and effectiveness of our proposed approach. After extensive experimentation, we
conclude that the framework shows promise for further research and specialized
commercial applications.
