Text data has continuous growth of volumes of data, automate extract ion of implicit, previously unknown, and potentially useful information becomes more necessary to properly utilize this vast source of knowledge. Text mining corresponds to the extension of the data mining approach to textual data and is concerned with various tasks, such as extraction of information implicitly contained in collection of documents, or similarity-based structuring. This paper provides the reader with a very brief introduction to some of the theory and methods of text data mining. The intent of this paper is to introduce some of the current text mining methods that are employed within this discipline area. In this paper we provide some of methods of text datamining.

Text Mining, Text Mining Text Processing, Methods Text, Document clustering

[1]. BAEZA-YATES, R. AND RIBEIRO-NETO, B. (1990). Modern Information Retrieval. Addison Wesley.
[2].BERRY, M.W.(2003) . Survey of Text Mining: Clustering, classification and Retrieval (Hardcover). springer.
[3] PORTER, M.F. (1980). Algorithm for suffix striping, Program, 130-137.
[4]. DEERVESTER, S., DUMAIS, S.T., FURNAS, G. W., AND LANDAUER, T.K. (1990). Indexing by latent semantic analysis. Journal of the Am. Soc. for Information Science 41, 6, 391-407.
[5]. DUDA, R.O., HART, P.E., AND STORK, D.G. (2000). Pattern Classification, Second ed. Wiley Interscience. MR1802993.
[6]. MORRIS, S.A. AND YEN, G.G. (2004). Crossmaps: visualization of overlapping relationships in collections of journal papers. Proceedings of the National Academy of Sciences of the United states of America supplement 1 101, 5291-5296.
[7] Fayyad U, Piatetsky-Shapro G, and Smyth P 1996 from data mining to knowledge discovery: an overview. In Fayyad U, Piatetsky Shapiro G, Smyth P, and Uthurusamy R (eds) Advances in Knowledge Discovery and Data Mining. Cambridge, MA, AAAI/MIT press:1-34.
[8] Dunham, M.H. (2003). Data Mining- Interdictory and Advanced Topics. Prentice-Hall, NewJersey.
[9] Witten, I.A. and Frank, E. (2000). Data Mining-Practicall Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, SanFrancisco.
[10] Groth, R. (2000). Data Mining – Building competitive Advantage. Prentice-Hall, NewJersey.
[11] G. Giuffrida, W.W.Chu, D.M.Hassens, NOAH: An Algorithm for Mining Classification Rules from Datasets with Large Attribute Space. In Proceedings of 12th International Conference on Extending Database(EDBT), Konsta, Germenay, March2000.
[12] Q. Zou, W.W. Chu, D. Johnson, H.Chiu, A Pattern Decomposition Algorithm for Finding All frequent Patterns in Large Datasets. ICDM2001:673-674.
[13] W.W. Chu, K.Ching, C.C.Hsu, H.Yau, An Error based Conceptual Clustering Method for Providing Approximate Query Answers. Communications of the ACM, 39(13), December,1996.
[14] J.Han, J. Pei, Y.Yin, Mining Frequent Patterns without Candidate Generation. 2000 ACM SIGMOD Intl. Conference on Management ofData.
[15] C.M.Ho, P.H.Huang, J.lew, J.D.Mai, V.Lee, Y.C.Tai, Intelligent System Capable of Sensing-Computing-Actuating, Keynote Address, 4th Intl. Conference on Intelligent Materials, Society of Non-Traditional Technology. Tokyo, Japan, October 1998.