Data and Text Mining

Paper Summary:

The paper discusses how the many advances in data and text mining are already revolutionizing the librarian profession. The paper explores how the ability of data mining tools to extract, transfer and load (ETL) massive amounts of data at a single time, is changing how all tasks in an organization get completed.

Outline:
Executive Summary
Content Integration Is Key
Data Mining
i) Principles of Data Mining
ii) Data Mining Timeline
Data Mining Implications for Librarianship
Text Mining
i) Text Mining Timeline
ii) Data Mining versus Text Mining
iii) Mining Blogs: An Example of How Text Mining Works
Text Mining Implications for Librarianship
Conclusion

From the Paper:

"At the intersection of text mining, linguistic analysis, statistical analysis, and latent semantic indexing techniques (Wikipedia Latent Semantic Indexing 2006). is the future of text mining that has the power to discover and report trending in highly unstructured content. At the center of text-mining's' rapid growth is the increasing sophistication of Natural Language Processing (CRM Buyer 2005). IBM and their significant research efforts in natural language processing are well documented on their website, as are the efforts and investments Microsoft is making."

Sample of Sources Used:

• ACTL99 Proceedings (1999). Untangling Data Text Mining. Retreived from the Internet February 13, 2006. http://www.sims.berkeley.edu/~hearst/papers/acl99/acl99-tdm.html
• Attensity (2006). .Competitive Analysis and Update. LWC Research & Consultancy. Irvine, CA January, 2006.
• Bell & Knox (2005). Content Integration Will Become a Top Priority by 2006. Toby Bell and Rita Knox. Gartner Group, Turnbull, CT. February 17, 2005
• Berry (2004) - Survey of Text Mining Clustering, Classification, and Retrieval Berry, Michael W. (Ed.) 2004, XVII, 244 p. 57 illus., Hardcover ISBN: 0-387-95563-1
• Centor (2006). Competitive Analysis and Update. LWC Research & Consultancy. Irvine, CA January, 2006.

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