Text document clustering using statistical integrated graph based sentence sensitivity ranking algorithm

Abstract

The proposed methodology employs a novel statistical integrated graph-based sentence sensitivity ranking algorithm for text document clustering. Clustering of documents is a task of grouping a document automatically into a list of meaningful clusters; in order for the documents inside a group to share the same topic. In this paper, first, a novel integrated graph-based methodology using the sentence sensitivity ranking is proposed to extract keyphrases from the documents. In the standard statistical approach, keyphrases are extracted on the basis of the sentence sensitivity ranking; and in the graph-based method, the candidate keyphrases are automatically created as graphs by applying the sentence sensitivity ranking. With the aid of the top listed keyphrases, the documents clustering are carried out by implementing the proposed sentence sensitivity ranking algorithm. The simulation results reveal that the proposed graph-based text document clustering using statistical integrated graph-based sentence sensitivity ranking algorithm obtained the best results for clustering the text documents.