A Survey Paper on Text Mining Techniques

A Survey Paper on Text Mining Techniques

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2016 by IJETT Journal
Volume-40 Number-4
Year of Publication : 2016
Authors : C.Uma, S.Krithika, C.Kalaivani
DOI :  10.14445/22315381/IJETT-V40P237

Citation 

C.Uma, S.Krithika, C.Kalaivani"A Survey Paper on Text Mining Techniques", International Journal of Engineering Trends and Technology (IJETT), V40(4),225-229 October 2016. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
Text mining, also referred to as text data mining, roughly equivalent to text analytics, refers to the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. There are many techniques for text mining. In this paper we describe the techniques, Information Extraction, Information retrieval, Query processing, Natural Language processing, Categorization, Clustering. We also discuss future challenges of this area using different techniques, particularly rough set based text mining techniques, improvements and research directions in this paper.

 References

[1] Berry Michael W., (2004), “Automatic Discovery of Similar Words”, in “Survey of Text Mining: Clustering, Classification and Retrieval”, Springer Verlag, New York, LLC, 24-43.
[2] Navathe, Shamkant B., and Elmasri Ramez, (2000), “Data Warehousing And Data Mining”, in “Fundamentals of Database Systems”, Pearson Education pvt Inc, Singapore, 841-872.
[3] Shaidah Jusoh and Hejab M. Alfawareh, “Techniques, Applications and Challenging Issue in Text Mining”, IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 6, No 2, November 2012, ISSN (Online): 1694-0814.
[4] Fred Popowich, “Using Text Mining and Natural Language Processing for Health Care Claims Processing”
[5] Hejab M. Alfawareh, Shaidah Jusoh, “Resolving Ambiguous Entity through Context Knowledge and Fuzzy Approach”, International Journal on Computer Science and Engineering (IJCSE), ISSN : 0975-3397 Vol. 3 No. 1 Jan 2011
[6] D. Cutting, D. Karger, J. Pedersen, J. Tukey. Scatter/Gather: ACluster-based Approach to Browsing Large Document Collections.ACM SIGIR Conference, 1992.
[7] L. Baker, A. McCallum. Distributional Clustering of Words for Text Classification,ACM SIGIR Conference , 1998.
[8] R. Bekkerman, R. El-Yaniv, Y. Winter, N. Tishby. On Feature Dis-tributional Clustering for Text Categorization.ACM SIGIR Con-ference, 2001.
[9] K. Nigam, A. McCallum, S. Thrun, T. Mitchell. Learning to classify text from labeled and unlabeled documents.AAAI Conference,1998.

Keywords
Data mining, Text mining, Rough sets, Classification, Summarization, and Text categorization, Clustering, Information Extraction, Information Retrieval.