CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

A New Approach for Extraction of Pattern Frames in Text Mining

by B. Sankara Babu, K. Rajasekhar Rao, P.satheesh
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 103 - Number 7
Year of Publication: 2014
Authors: B. Sankara Babu, K. Rajasekhar Rao, P.satheesh
10.5120/18087-9130

B. Sankara Babu, K. Rajasekhar Rao, P.satheesh . A New Approach for Extraction of Pattern Frames in Text Mining. International Journal of Computer Applications. 103, 7 ( October 2014), 21-24. DOI=10.5120/18087-9130

@article{ 10.5120/18087-9130,
author = { B. Sankara Babu, K. Rajasekhar Rao, P.satheesh },
title = { A New Approach for Extraction of Pattern Frames in Text Mining },
journal = { International Journal of Computer Applications },
issue_date = { October 2014 },
volume = { 103 },
number = { 7 },
month = { October },
year = { 2014 },
issn = { 0975-8887 },
pages = { 21-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume103/number7/18087-9130/ },
doi = { 10.5120/18087-9130 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:33:55.520191+05:30
%A B. Sankara Babu
%A K. Rajasekhar Rao
%A P.satheesh
%T A New Approach for Extraction of Pattern Frames in Text Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 103
%N 7
%P 21-24
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Due to the rapid growth in World Wide Web and data availability, text mining has become one of the most important fields in data mining. Text mining refers to the technique which is useful to find the information from a huge volume of digital documents. Many existing text mining methods follow the term based approaches. Pattern evolution methods are employed to perform the same concept of tasks . This paper presents a new approach for extraction of the pattern frames in text mining.

References
  1. N. Zhong, Y. Li, and S. T. Wu. Effective pattern discovery for text mining. IEEE Transactions on Knowledge and Data Engineering, DOI: http://doi. ieeecomputersociety. org/10. 1109/TKDE. 2010 K.
  2. R. Agrawal and R. Srikant, "Fast Algorithms for Mining Association Rules in Large Databases," Proc. 20th Int'l Conf. Very Large Data Bases (VLDB '94), pp. 478-499, 1994.
  3. H. Ahonen, O. Heinonen, M. Klemettinen, and A. I. Verkamo, "Applying Data Mining Techniques for Descriptive Phrase in Digital Document Collections," Proc. IEEE Int'l Forum on Research and Technology Advances in Digital Libraries (ADL '98), pp. 2-11, 1998.
  4. R. Baeza-Yates and B. Ribeiro-Neto, Modern Information Retrieval. Addison Wesley, 1999.
  5. J. S. Park, M. S. Chen, and P. S. Yu, "An Effective Hash-Based Algorithm for Mining Association Rules," Proc. ACM SIGMOD Int'l Conf. Management of Data (SIGMOD '95), pp. 175-186, 1995.
  6. F. Sebastiani, "Machine Learning in Automated Text Categorization," ACM Computing Surveys, vol. 34, no. 1, pp. 1-47, 2002.
  7. M. F. Caropreso, S. Matwin, and F. Sebastiani, "Statisticals Phrases in Automated Text Categorization," Technical Report IEI-B4-07- 2000, Instituto di Elaborazione dell'Informazione, 2000.
  8. S. T. Dumais, "Improving the Retrieval of Information from External Sources," Behavior Research Methods, Instruments, and Computers, vol. 23, no. 2, pp. 229-236, 1991.
  9. M. Seno and G. Karypis, "Slpminer: An Algorithm for Finding Frequent Sequential Patterns Using Length-Decreasing Support Constraint," Proc. IEEE Second Int'l Conf. Data Mining (ICDM '02),pp. 418-425, 2002.
  10. J. Han and K. C. -C. Chang, "Data Mining for Web Intelligence," Computer, vol. 35, no. 11, pp. 64-70, Nov. 2002.
  11. J. Han, J. Pei, and Y. Yin, "Mining Frequent Patterns without Candidate Generation," Proc. ACM SIGMOD Int'l Conf. Management of Data (SIGMOD '00), pp. 1-12, 2000.
  12. Y. Huang and S. Lin, "Mining Sequential Patterns Using Graph Search Techniques," Proc. 27th Ann. Int'l Computer Software and Applications Conf. , pp. 4-9, 2003.
  13. D. D. Lewis, "An Evaluation of Phrasal and Clustered Representations on a Text Categorization Task," Proc. 15th Ann. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR '92), pp. 37-50, 1992.
  14. T. Joachims, "A Probabilistic Analysis of the Rocchio Algorithm with tfidf for Text Categorization," Proc. 14th Int'l Conf. Machine Learning (ICML '97), pp. 143-151, 1997.
  15. T. Joachims, "Text Categorization with Support Vector Machines: Learning with Many Relevant Features," Proc. European Conf. Machine Learning (ICML '98),, pp. 137-142, 1998.
  16. T. Joachims, "Transductive Inference for Text Classification Using Support Vector Machines," Proc. 16th Int'l Conf. Machine Learning (ICML '99), pp. 200-209, 1999.
  17. W. Lam, M. E. Ruiz, and P. Srinivasan, "Automatic Text Categorization and Its Application to Text Retrieval," IEEE Trans. Knowledge and Data Eng. , vol. 11, no. 6, pp. 865-879, Nov. /Dec. 1999. 42 IEEE TRANSACTIONS ON International Journal of Advances in Science Engineering and Technology Volume- 1, Issue- 1.
  18. Words Sequence Pattern Mining Using Pattern Taxonomy Model Knowledge and Data Engineering, Vol. 24, No. 1, January 2012
Index Terms

Computer Science
Information Sciences

Keywords

Text mining pattern frames information

Powered by PhDFocusTM