Overview
- Editors:
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Michael W. Berry
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Department of Computer Science, University of Tennessee, USA
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Malu Castellanos
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Hewlett-Packard Laboratories, Palo Alto, USA
Overview of current methods and software for text mining
Experts from academia and industry share their experiences in solving large-scale retrieval and classification problems
Highlights open research questions in document categorization and clustering, and trend detection
Describes new application problems in areas such as email surveillance and anomaly detection
Includes supplementary material: sn.pub/extras
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Table of contents (12 chapters)
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Clustering
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- Pierre Senellart, Vincent D. Blondel
Pages 25-44
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- Dimitrios Zeimpekis, Efstratios Gallopoulos
Pages 45-64
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- Jacob Kogan, Charles Nicholas, Mike Wiacek
Pages 65-85
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- Loulwah AlSumait, Carlotta Domeniconi
Pages 87-105
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Document Retrieval and Representation
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- Mei Kobayashi, Masaki Aono
Pages 109-127
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- Zhonghang Xia, Guangming Xing, Houduo Qi, Qi Li
Pages 129-144
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Email Surveillance and Filtering
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- Brett W. Bader, Michael W. Berry, Murray Browne
Pages 147-163
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- Wilfried N. Gansterer, Andreas G. K. Janecek, Robert Neumayer
Pages 165-183
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Anomaly Detection
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- Edward G. Allan, Michael R. Horvath, Christopher V. Kopek, Brian T. Lamb, Thomas S. Whaples, Michael W. Berry
Pages 203-217
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- Mostafa Keikha, Narjes Sharif Razavian, Farhad Oroumchian, Hassan Seyed Razi
Pages 219-232
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Back Matter
Pages 233-240
About this book
As we enter the third decade of the World Wide Web (WWW), the textual revolution has seen a tremendous change in the availability of online information. Finding inf- mation for just about any need has never been more automatic—just a keystroke or mouseclick away. While the digitalization and creation of textual materials continues at light speed, the ability to navigate, mine, or casually browse through documents too numerous to read (or print) lags far behind. What approaches to text mining are available to ef?ciently organize, classify, label, and extract relevant information for today’s information-centric users? What algorithms and software should be used to detect emerging trends from both text streamsandarchives?Thesearejustafewoftheimportantquestionsaddressedatthe Text Mining Workshop held on April 28, 2007, in Minneapolis, MN. This workshop, the ?fth in a series of annual workshops on text mining, was held on the ?nal day of the Seventh SIAM International Conference on Data Mining (April 26–28, 2007). With close to 60 applied mathematicians and computer scientists representing universities, industrial corporations, and government laboratories, the workshop f- tured both invited and contributed talks on important topics such as the application of techniques of machine learning in conjunction with natural language processing, - formation extraction and algebraic/mathematical approaches to computational inf- mation retrieval. The workshop’s program also included an Anomaly Detection/Text Mining competition. NASA Ames Research Center of Moffett Field, CA, and SAS Institute Inc. of Cary, NC, sponsored the workshop.
Editors and Affiliations
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Department of Computer Science, University of Tennessee, USA
Michael W. Berry
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Hewlett-Packard Laboratories, Palo Alto, USA
Malu Castellanos