Powerful Tool to Expand Business Intelligence: Text Mining

With the extensive inclusion of document, especially text, in the business systems, data mining does not cover the full scope of Business Intelligence. Data mining cannot deliver its impact on extracting useful details from the large collection of unstructured and semi-structured written materials based on natural languages. The most pressing issue is to draw the potential business intelligence from text. In order to gain competitive advantages for the business, it is necessary to develop the new powerful tool, text mining, to expand the scope of business intelligence. In this paper, we will work out the strong points of text mining in extracting business intelligence from huge amount of textual information sources within business systems. We will apply text mining to each stage of Business Intelligence systems to prove that text mining is the powerful tool to expand the scope of BI. After reviewing basic definitions and some related technologies, we will discuss the relationship and the benefits of these to text mining. Some examples and applications of text mining will also be given. The motivation behind is to develop new approach to effective and efficient textual information analysis. Thus we can expand the scope of Business Intelligence using the powerful tool, text mining.




References:
[1] B. de Ville, "Microsoft Data Mining: Integrated Business Intelligence
for e-Commerce and Knowledge Management", Boston: Digital Press,
2001.
[2] P. Bergeron, C. A. Hiller, "Competitive intelligence", in B. Cronin,
Annual Review of Information Science and Technology, Medford, N.J.:
Information Today, vol. 36, chapter 8, 2002.
[3] M. J. A. Berry, G. Linoff, "Data Mining Techniques: For Marketing,
Sales, and Customer Relationship Management", Wiley Computer
Publishing, 2nd edition, 2004.
[4] X. Wu, P. S. Yu, G. Piatetsky-Shapiro, "Data Mining: How Research
Meets Practical Development?" Knowledge and Information Systems,
vol. 5(2):248-261, 2003.
[5] D. Pyle, "Business Modeling and Data Mining", Morgan Kaufmann,
San Francisco, CA, 2003.
[6] M. H. Dunham, "Data Mining-Introductory and Advanced Topics",
Prentice Hall, 2005.
[7] R. P. Hart, "The Text Analysis Program", DICTION 5.0, Thousand
Oaks, Calif.: Sage.
[8] H. Liu, H. Motoda, L. Yu, "Feature Extraction, Selection, and
Construction", in N. Ye, editor, The Handbook of Data Mining, pp. 22-
41. Lawrence Erlbaum Associates, Inc., Mahwah, NJ, 2003.
[9] R. Baeza-Yates, B. Ribeiro-Neto, "Modern Information Retrieval",
Addison- Wesley Longman Publishing Company, 1999.
[10] Y. Yang, J. O. Pederson, "A comparative study on feature selection in
text categorization", Morgan Kaufmann, 1997, 412-420.
[11] S. T. Dumais, "Latent Semantic Analysis", in B.Cronin (ed.), Annual
Review of Information Science and Technology, vol.38, chapter 4,
Medford, N.J.: Information Today, 2004, pp. 189-230.
[12] C. Date, "Introduction to Database Systems", 8th ed., Upper Saddle
River, N.J.: Pearson Addison Wesley, 2003.
[13] E. Riloff, W. Lehnert, "Automatically Constructing a Dictionary for
InformationExtraction Tasks," Proceedings of the Eleventh Annual
Conference of Machine Learning, 25-32, 1994.
[14] D. R. Swanson, "Two medical literatures that are logically but not
bibliographically connected", JASIS, vol. 38(4), 1987, 228-223.