Mining Sequential Pattern of Data in Textual Document Using Data Mining Classification Technique

Authors

  • J. Jayasudha Assistant Professor, Department of Computer Science, Kovai Kalaimagal College of Arts and Science, Coimbatore, Tamil Nadu, India
  • A. Christina Esther Assistant Professor, Department of Computer Science, Kovai Kalaimagal College of Arts and Science, Coimbatore, Tamil Nadu, India

DOI:

https://doi.org/10.51983/ajcst-2019.8.S1.1961

Keywords:

Text Mining, Textual Document, Sequential Analyses, Personalize and Abnormal Behavior

Abstract

Text document were transmitted over the internet for the text communication. So they were occurred many problems like repeated text occurred because of same data were provided in the internet. To characterize and extracting that is a most critical task for the researchers. Many researchers were characterized and applied in many fields like real-life scenarios, such as real-time monitoring on abnormal user behaviors, etc. In this case to detect and characterize the personalized behavior of the user were provide some drawbacks. To solve this problem, this paper analyzing the sequential data and characterize the user behavior with the help of the data mining sequential pattern matching algorithm.

References

N. Jain and V. Srivastava, "Data Mining techniques: A survey paper," IJRET: International Journal of Research in Engineering and Technology, vol. 2, no. 11, pp. 2319-1163, 2013.

N. Padhy, Dr. Mishra, and R. Panigrahi, "The survey of data mining applications and feature scope," arXiv preprint arXiv, pp. 1211.5723, 2012.

J. Zhu, K. Wang, Y. Wu, Z. Hu, and H. Wang, "Mining User-Aware Rare Sequential Topic Patterns in Document Streams," IEEE Trans. Knowl. Data Eng., vol. 28, no. 7, pp. 1790-1804, 2016.

G. Chandrashekar and F. Sahin, "A survey on feature selection methods," in Comput. Electr. Eng., vol. 40, pp. 16–28, 2014.

J. Han, J. Pei, and M. Kamber, "Data mining: concepts and techniques," Elsevier, 2011.

Y. Li, A. Algarni, and N. Zhong, "Mining positive and negative patterns for relevance feature discovery," in Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 753-762. ACM, 2010.

N. Zhong, Y. Li, and S.-T. Wu, "Effective pattern discovery for text mining," IEEE transactions on knowledge and data engineering, vol. 24, no. 1, pp. 30-44, 2012.

R. Kostoff and N. Ronald, "Method for data and text mining and literature-based discovery," U.S. Patent 6,886,010, issued April 26, 2005.

T. A. Pawar and N. D. Karande, "Effective Pattern Discovery For Text Mining Using Pattern Based Approach," International Journal of Advance Research in Computer Science and Management Studies, ISSN, pp. 2321-7782, 2014.

S. Loh, L. K. Wives, and J. P. M. de Oliveira, "Concept-based knowledge discovery in texts extracted from the web," ACM SIGKDD Explorations Newsletter, vol. 2, no. 1, pp. 29-39, 2000.

M. A. Tobji, B. Bach, B. Ben Yaghlane, and K. Mellouli, "A new algorithm for mining frequent itemsets from evidential databases," in Proceedings of IPMU, vol. 8, pp. 1535-1542, 2008.

R. Agrawal and R. Srikant, "Fast algorithms for mining association rules," in Proc. ACM SIGMOD Int. Conf. on Management of Data, Minneapolis, MN, 1994.

X. Li and B. Liu, "Learning to classify texts using positive and unlabeled data," in Proc. 18th Int. Joint Conf. Artif. Intell, pp. 587–592, 2003.

R. Srikant and R. Agrawal, "Mining sequential patterns: Generalizations and performance improvements," in International Conference on Extending Database Technology, pp. 1-17. Springer, Berlin, Heidelberg, 1996.

S. Lodhi, P. Arya, and D. Vishwakarma, "Frequent Itemset Mining Technique in Data Mining," International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), vol. 1, no. 5, pp. 395-402, 2012.

Downloads

Published

08-02-2019

How to Cite

Jayasudha, J., & Christina Esther, A. (2019). Mining Sequential Pattern of Data in Textual Document Using Data Mining Classification Technique. Asian Journal of Computer Science and Technology, 8(S1), 41–45. https://doi.org/10.51983/ajcst-2019.8.S1.1961