Enhanced Semantic Preserved Concept Based Mining Model for Enhancing Document Clustering
Resmi Ramachandran Pillai
Resmi Ramachandran Pillai, Computer Science and Engineering,Anna University,Tamil Nadu, India.
Manuscript received on December 01, 2014. | Revised Manuscript Received on December 09, 2014. | Manuscript published on December 20, 2014. | PP: 29-34 | Volume-3 Issue-1, December 2014. | Retrieval Number: A0562123114/2014©BEIESP
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Abstract: The project “Enhanced semantic preserved concept based mining model for enhancing document clustering ” proposes the enhancement of data mining model for efficient informaion retreival . Concept based mining model is a challenging and a red hot field in the current scenario and has great importance in text categorization applications. A lot of research work has been done in this field but there is a need to categorize a collection of text documents into mutually exclusive categories by extracting the concepts or features using supervised learning paradigm and different classification algorithms. This project aims to Develop a concept based mining model for preserving the meaning of sentence using semantic net & synonym dictionary. The new concept definition can be expressed in the form of a triplet .This triplet is the basic unit for the processing and preprocessing tasks. For increasing the performance, SVD (Singular Value Decomposition) is used.
Keywords: SVD, Concept, Categories, algorithms, clustering.