Asian Journal of Computer Science and Technology (AJCST)
Mining Sequential Pattern of Data in Textual Document Using Data Mining Classification Technique
Author : J. Jayasudha and A. Christina EstherVolume 8 No.1 Special Issue:February 2019 pp 41-45
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.
Keywords
Text Mining, Textual Document, Sequential Analyses, Personalize and Abnormal Behavior
Full Text:
References
[1] Jain, Nikita, and Vishal Srivastava, “Data Mining techniques: A survey paper”, IJRET: International Journal of Research in Engineering and Technology,Vol. 2, No. 11, pp. 2319-1163,2013.
[2] Padhy, Neelamadhab, Dr. Mishra, and RasmitaPanigrahi, “The survey of data mining applications and feature scope”, arXiv preprint arXiv, pp. 1211.5723, 2012.
[3] Zhu, Jiaqi, Kaijun Wang, Yunkun Wu, Zhongyi Hu, and Hongan Wang, “Mining User-Aware Rare Sequential Topic Patterns in Document Streams”, IEEE Trans. Knowl. Data Eng, Vol. 28, No. 7, pp. 1790-1804, 2016
[4] G. Chandrashekar and F. Sahin, “Asurvey on feature selection methods,” in Comput. Electr. Eng., vol. 40, pp. 16–28, 2014.
[5] Han, Jiawei, Jian Pei, and MichelineKamber, “Data mining: concepts and techniques”, Elsevier, 2011.
[6] Li, Yuefeng, AbdulmohsenAlgarni, and NingZhong, “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.
[7] Zhong, Ning, Yuefeng Li, and Sheng-Tang Wu, “Effective pattern discovery for text mining”, IEEE transactions on knowledge and data engineering, Vol. 24, No. 1, pp. 30-44, 2012.
[8] Kostoff, and N.Ronald, “Method for data and text mining and literature-based discovery”, U.S. Patent 6,886,010, issued April 26, 2005.
[9] 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.
[10] Loh, Stanley, Leandro Krug Wives, and José Palazzo 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.
[11] Tobji, MA Bach, B. Ben Yaghlane, and KhaledMellouli, “A new algorithm for mining frequent itemsets from evidential databases”, In Proceedings of IPMU, Vol. 8, pp. 1535-1542. 2008.
[12] R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proc. ACM SIGMOD Int. Conf. on Management of Data, Minneapolis, MN, 1994.
[13] 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.
[14] Srikant, Ramakrishnan, and RakeshAgrawal, “Mining sequential patterns: Generalizations and performance improvements”, In International Conference on Extending Database Technology, pp. 1-17. Springer, Berlin, Heidelberg, 1996.
[15] Lodhi, Sanjaydeep Singh, PremnarayanArya, and Dilip 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.