Research Article Open Access

Significant Term List Based Metadata Conceptual Mining Model for Effective Text Clustering

S. Koteeswaran1, J. Janet2 and E. Kannan1
  • 1 Vel Tech Dr. RR and Dr. SR Technical University, India
  • 2 Dr. MGR University, India


As the engineering world are growing fast, the usage of data for the day to day activity of the engineering industry also growing rapidly. In order to handle and to find the hidden knowledge from huge data storage, data mining is very helpful right now. Text mining, network mining, multimedia mining, trend analysis are few applications of data mining. In text mining, there are variety of methods are proposed by many researchers, even though high precision, better recall are still is a critical issues. In this study, text mining is focused and conceptual mining model is applied for improved clustering in the text mining. The proposed work is termed as Meta data Conceptual Mining Model (MCMM), is validated with few world leading technical digital library data sets such as IEEE, ACM and Scopus. The performance derived as precision, recall are described in terms of Entropy, F-Measure which are calculated and compared with existing term based model and concept based mining model.

Journal of Computer Science
Volume 8 No. 10, 2012, 1660-1666


Submitted On: 20 August 2012 Published On: 25 August 2012

How to Cite: Koteeswaran, S., Janet, J. & Kannan, E. (2012). Significant Term List Based Metadata Conceptual Mining Model for Effective Text Clustering. Journal of Computer Science, 8(10), 1660-1666.

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  • Meta data conceptual mining model
  • clustering
  • text mining
  • data mining