Comparison between Clustering Algorithms Based On Ontology Based Text Mining Techniques

S. Suguna, B. Gomathi


Large number of documents is grouped according to their similarities. The Text Mining methods have been proposed to solve the problem by automatically classifying text documents, mainly in English. But this method has some limitations when dealing with non-English language texts, e.g., Chinese research proposals. An ontology based text MINING approach is to cluster the documents based on their similarities. For ontology making in text documents, the clustering algorithm is need to be applied. This paper focuses on two clustering techniques SOM and DVA for clustering documents based on their similarities. SOM and DVA algorithms are applied after the preprocessing process with doc-uments such as Stop Word Removal, Stemming. DVA includes Vector Dimension Reduction also. The proposed Ontology based text mining technique is efficient and effective in terms of time, reliability and quantity. DVA out performs than SOM in terms of all the factors.


Keywords: Text Mining, Ontology, OTMM, Clustering, SOM and DVA Algorithm

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