Ontology Based Document Clustering - An Efficient Hybrid Approach

Recent research results show that ontology can be used to improve the accuracy of document clustering. Previous studies mainly focused on the preprocessing part of text document using ontology. In this paper, we propose a hybrid approach, concentrating on both the preprocessing task as well as the clustering algorithm. This is with an objective of reducing the number of features and execution time, eliminate synonymous problems and enhance the accuracy of clustering. Cosine similarity is used as similarity measure. The preprocessing part uses a WordNet Ontology based feature extraction method. In clustering, the initial centroids are found by applying the Red Black Tree based sorting method. The data points are allocated to the suitable clusters using a novel approach, by maintaining the path of similarity between data points and nearest cluster centroids. Experimental results on some of the existing clustering algorithms with cosine similarity are compared with our novel clustering technique. Results show that the proposed hybrid approach executes better on the Newsgroup dataset with considerable improvements in dimensionality reduction, running time, and accuracy.