Hybrid Intelligent Similarity Measure for Effective Text Document Clustering Using Neural Network Algorithm

Authors

  • R. Preethi  R.M.K Engineering College, Kavaraipettai, Chennai, Tamil Nadu, India}Sathyabama University, Jeppiaar Nagar, Rajiv Gandhi Road, Chennai, Tamil Nadu, India
  • K. Selvi   

Keywords:

World Wide Web, WordSim353, Clustering, Neural Network, Cyber terrorism investigation, Verb-Argument Structures, DIG, DIGBC, LSI, PCA, SVD, RFP, Textmining, Document Clustering Similarity Measure, Artificial Intelligence

Abstract

Extensive use of World Wide Web for information search using popular search engines has turned many researchers to focus on text mining issues. Natural Language Processing required effective methods to capture the actual requirements of the user during Machine Learning. Application of genetic algorithm and similarity measure for text mining during document clustering yield significant results for WordSim353 data sets. Experiments show that application of Echo State Neural Network and Radial Basis Function to the training data set gives better clustering of text documents based on the stored weights in order to avoiding retrieval of irrelevant documents.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2017-06-30

Issue

Volume 2, Issue 3, May-June-2017

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Research Articles

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How to Cite

[1]
R. Preethi, K. Selvi , " Hybrid Intelligent Similarity Measure for Effective Text Document Clustering Using Neural Network Algorithm, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 3, pp.833-839, May-June-2017.