Tapping the power of text mining

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Tapping the power of text mining

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Communications of the ACM archive
Volume 49 , Issue 9 (September 2006) table of contents
Privacy and security in highly dynamic systems

Pages: 76 - 82

Year of Publication: 2006

ISSN:0001-0782

Authors

Weiguo Fan	Virginia Polytechnic Institute and State University, Blacksburg, VA
Linda Wallace	Virginia Polytechnic Institute and State University, Blacksburg, VA
Stephanie Rich	Virginia Polytechnic Institute and State University, Blacksburg, VA
Zhongju Zhang	University of Connecticut, Stamford, CT

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ACM New York, NY, USA

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Downloads (6 Weeks): 60, Downloads (12 Months): 565, Citation Count: 5

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ABSTRACT

Sifting through vast collections of unstructured or semistructured data beyond the reach of data mining tools, text mining tracks information sources, links isolated concepts in distant documents, maps relationships between activities, and helps answer questions.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Kurt D. Bollacker , Steve Lawrence , C. Lee Giles, A system for automatic personalized tracking of scientific literature on the Web, Proceedings of the fourth ACM conference on Digital libraries, p.105-113, August 11-14, 1999, Berkeley, California, United States [doi>10.1145/313238.313270]
	2	ClearForest. ClearForest-Dow Chemical Case Study. ClearForest Corp., Waltham, MA, 2004; www.clearforest.com/Customers/Dow.asp.
	3	Creese, G. Duo-mining: Combining Data and Text Mining (Sept. 2004); www.dmreview.com/article_sub.cfm?articleId=1010449.
	4	Michael Gordon , Robert K. Lindsay , Weiguo Fan, Literature-based discovery on the World Wide Web, ACM Transactions on Internet Technology (TOIT), v.2 n.4, p.261-275, November 2002 [doi>10.1145/604596.604597]
	5	Jiawei Han , Russ B. Altman , Vipin Kumar , Heikki Mannila , Daryl Pregibon, Emerging scientific applications in data mining, Communications of the ACM, v.45 n.8, August 2002 [doi>10.1145/545151.545179]
	6	Hearst, M. What Is Text Mining?; www.sims.berkeley.edu/~hearst/text-mining.html.
	7	KartOO Metasearch Engine. KartOO S.A., Clermont Ferrand, France; www.kartoo.com.
	8	Getting answers to natural language questions on the web, Journal of the American Society for Information Science and Technology, v.53 n.5, p.359-364, May 2002 [doi>10.1002/asi.10053]
	9	Swanson, D. Two medical literatures that are logically but not bibliographically connected. Journal of the American Society for Information Science 38, 4 (1987), 228--233.
	10	Yiming Yang , Jan O. Pedersen, A Comparative Study on Feature Selection in Text Categorization, Proceedings of the Fourteenth International Conference on Machine Learning, p.412-420, July 08-12, 1997

CITED BY 5

	Anna Stavrianou , Periklis Andritsos , Nicolas Nicoloyannis, Overview and semantic issues of text mining, ACM SIGMOD Record, v.36 n.3, September 2007
	Henning Baars , Hans-George Kemper, Management Support with Structured and Unstructured Data-An Integrated Business Intelligence Framework, Information Systems Management, v.25 n.2, p.132-148, March 2008
	Katerina Papadaki , Despina Polemi, A meta-process for information security risk management, International Journal of Electronic Security and Digital Forensics, v.1 n.4, p.336-343, November 2008
	Jonas Poelmans , Paul Elzinga , Stijn Viaene , Marc M. Van Hulle , Guido Dedene, Gaining insight in domestic violence with Emergent Self Organizing Maps, Expert Systems with Applications: An International Journal, v.36 n.9, p.11864-11874, November, 2009
	Katerina Papadaki , Nineta Polemi, Towards a knowledge-sharing approach for Information Security Risk Management, Proceeding of the 2008 conference on Knowledge-Based Software Engineering: Proceedings of the Eighth Joint Conference on Knowledge-Based Software Engineering, p.453-461, June 29, 2008