An Efficient Pattern Discovery Over Long Text Patterns

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2014 by IJETT Journal
Volume-10 Number-9
Year of Publication : 2014
Authors : T.Ravi Kiran , Ch.Sai Priyanka Rani , K.Bhoomika , K.Madhuri , L.Naresh


T.Ravi Kiran , Ch.Sai Priyanka Rani , K.Bhoomika , K.Madhuri , L.Naresh. "An Efficient Pattern Discovery Over Long Text Patterns", International Journal of Engineering Trends and Technology (IJETT), V10(9),470-473 April 2014. ISSN:2231-5381. published by seventh sense research group


There are several techniques are implemented for mining documents. In this text mining, still so many problems getting exact patterns in text mining. In this some of the techniques are adapted in text mining. In proposed system the temporal text mining approach is introduced. The system terms of its ability is evaluated to predict forthcoming events in the document. In this we present optimal decomposition of the time period associated with the given document set is discovered where each subinterval consists of consecutive time points having identical information content. Extraction of sequences of events from new and other documents based on the publication times of these documents has been shown to be extremely effective in tracking past events.


1. M.F. Caropreso, S. Matwin, and F. Sebastiani.Statistical Phrases in Automated Text Categorization, Technical Report IEI-B4-07- 2000, Instituto di Elaborazionedell’Informazione, 2000.
2. C. Cortes and V. Vapnik.Support-Vector Networks, Machine Learning, vol. 20, no. 3, pp. 273-297, 1995.
3. 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.
4. J. Han and K.C.-C.Chang.Data Mining for Web Intelligence, Computer, Vol. 35, No. 11, pp. 64-70, Nov. 2002.
5. 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.
6. 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.
7. N. Jindal and B. Liu.Identifying Comparative Sentences in Text Documents, Proc. 29th Ann. Int’l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR ’06), pp. 244-251, 2006.
8. 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.
9. T. Joachims. Text Categorization with Support Vector Machines: Learning with Many Relevant Features, Proc. European Conf. Machine Learning (ICML ’98),, pp. 137-142, 1998.