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Reseach Article

Aggregated Probabilistic Fuzzy Relational Sentence Level Expectation Maximization Clustering Algorithm for Efficient Text Categorization

by V.l. Kartheek, V. Chandra Sekhar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 107 - Number 13
Year of Publication: 2014
Authors: V.l. Kartheek, V. Chandra Sekhar
10.5120/18812-0388

V.l. Kartheek, V. Chandra Sekhar . Aggregated Probabilistic Fuzzy Relational Sentence Level Expectation Maximization Clustering Algorithm for Efficient Text Categorization. International Journal of Computer Applications. 107, 13 ( December 2014), 20-26. DOI=10.5120/18812-0388

@article{ 10.5120/18812-0388,
author = { V.l. Kartheek, V. Chandra Sekhar },
title = { Aggregated Probabilistic Fuzzy Relational Sentence Level Expectation Maximization Clustering Algorithm for Efficient Text Categorization },
journal = { International Journal of Computer Applications },
issue_date = { December 2014 },
volume = { 107 },
number = { 13 },
month = { December },
year = { 2014 },
issn = { 0975-8887 },
pages = { 20-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume107/number13/18812-0388/ },
doi = { 10.5120/18812-0388 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:40:58.774482+05:30
%A V.l. Kartheek
%A V. Chandra Sekhar
%T Aggregated Probabilistic Fuzzy Relational Sentence Level Expectation Maximization Clustering Algorithm for Efficient Text Categorization
%J International Journal of Computer Applications
%@ 0975-8887
%V 107
%N 13
%P 20-26
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Now a days, Text clustering becomes an important application to organize the data and to extract useful information from the available corpus. Many previous clustering techniques have difficulties in handling extreme outliers but fuzzy clustering algorithms tend to give them very small membership degree in surrounding clusters. In this paper we proposed an aggregated probabilistic Fuzzy relational sentence level expectation maximization clustering algorithm for efficient text categorization. It will give the accurate and maximum similarity by finding the relevance of sentences which belongs to a particular cluster. This technique leads to a fuzzy partition of the sentences and find out the accurate probability of the words belongs to a cluster. This algorithm is particularly used in finding maximum likelihood estimates of words in a given sentence. It gives the low search results with highest accuracy. The practical results show that the proposed method obtains better and accurate results for getting best sentence-wise text classification when compared with the existing methods.

References
  1. Jung-Yi Jiang, Ren-Jia Liou, and Shie-Jue Lee, Member, A Fuzzy Self-Constructing Feature Clustering Algorithm for Text Classification, March 2011, IEEE, VOL. 23, NO. 3.
  2. N. Slonim and N. Tishby. "The power of Word clusters for text classification," 23rd European Colloquium on Information Retrieval Research (ECIR), 2001.
  3. Neha Mehta, Mamta Kathuria, Mahesh Singh, "Comparison of conventional and fuzzy ClusteringTechniques: A survey", April 2014 IJARCCE, Vol. 2, Issue 1.
  4. G. Thilagavathi, J. Anitha, K. Nethra,"Sentence Similarity based Document Clustering using Fuzzy algorithm", March 2014, IJAFRC, Vol1, Issue 3.
  5. K. Jeyalakshmi1, R. Deepa2, M. Manjula," An Efficient Clustering Sentence-Level Text Using A Novel Hierarchical Fuzzy Relational Clustering Algorithm", February2014, IJARCCE, Vol. 3, Issue2.
  6. M. S. Yang," A Survey of Fuzzy clustering", October 1993, Vol 18, No 11.
  7. F. Pereira, N. Tishby, and L. Lee. "Distribution of A clustering of English words," 31st Annual Meeting of ACL, 1993, pages 183– 190.
  8. Hathaway RJ, Bezdek JC Recent convergence Results for the fuzzy C-means clustering Algorithms, Oct 1988. J Class 5:237-247.
  9. S. M. Jagatheesan1, V. Thiagarasu2," Development of Fuzzy based categorical Text Clustering Algorithm for Information Retrieval", January 2014, vol 2, issue 1.
  10. K. Nalini Dr. L. Jaba Sheela," Survey on Text
  11. Classification", IJIRAE, July 2014, Vol 1, Issue6
  12. Roventa, E. , Spircu, T. "Averaging Procedures in De fuzzification Processes, Fuzzy Sets and Systems ", 2003,136, pp. 375-385.
  13. S. J. Lee and C. S. Ouyang. "A neuro-fuzzys System modeling with self-constructing rule Generation and hybrid svd-based Learning," IEEE Transactions on Fuzzy Systems, June 2003, 11(3):341– 353.
  14. G. Salton and M. J. McGill. Introduction to Modern Retrieva,. McGraw-HillBook Company, 1983.
  15. F. Sebastiani. "Machine learning in automat Edtext categorization," ACM Computing Surveys, 34(1):1–47,
  16. March 2002.
  17. L. X. Wang. A Course in Fuzzy Systems and Control.
  18. Prentic-Hall International, Inc. , 1997.
  19. Y. Yang and J. O. Pedersen. "A comparative Study on feature selection in text categorization.
  20. R. Bekkerman, R. El-Yaniv, N. Tishby, and Y. Winter, "Distributional Word Clusters Versus Words for Text Categorization," J. Machine Learning Research, 2003, vol. 3, pp. 1183-1208
  21. L. D. Baker and A. McCallum, "Distributional Clustering of Words for Text classification, "Proc. ACM SIGIR, pp, 1998,Pages96-10.
Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy clustering corpus outliers.