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

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  • R. Preethi
  • K. Selvi

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.

Suggested Citation

  • R. Preethi & K. Selvi, 2017. "Hybrid Intelligent Similarity Measure for Effective Text Document Clustering Using Neural Network Algorithm," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 2(3), pages 833-839, June.
    • Handle: RePEc:jbh:ijsrcs:v2:y2017:i3:id:hcseit1723304
    Note: Article URL: https://ijsrcseit.com/CSEIT1723304
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