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

Authors(2) :-R. Preethi, K. Selvi

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.

Authors and Affiliations

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

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

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Publication Details

Published in : Volume 2 | Issue 3 | May-June 2017
Date of Publication : 2017-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 833-839
Manuscript Number : CSEIT1723304
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

R. Preethi, K. Selvi , "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 (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 3, pp.833-839, May-June-2017.
Journal URL : http://ijsrcseit.com/CSEIT1723304

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