Survey of Text Mining II

1. Front Matter

   Pages i-xv

   PDF

2. Clustering

   1. Cluster-Preserving Dimension Reduction Methods for Document Classification

      • Peg Howland, Haesun Park

      Pages 3-23

   2. Automatic Discovery of SimilarWords

      • Pierre Senellart, Vincent D. Blondel

      Pages 25-44

   3. Principal Direction Divisive Partitioning with Kernels and k-Means Steering

      • Dimitrios Zeimpekis, Efstratios Gallopoulos

      Pages 45-64

   4. Hybrid Clustering with Divergences

      • Jacob Kogan, Charles Nicholas, Mike Wiacek

      Pages 65-85

   5. Text Clustering with Local Semantic Kernels

      • Loulwah AlSumait, Carlotta Domeniconi

      Pages 87-105

3. Document Retrieval and Representation

   1. Vector Space Models for Search and Cluster Mining

      • Mei Kobayashi, Masaki Aono

      Pages 109-127

   2. Applications of Semidefinite Programming in XML Document Classification

      • Zhonghang Xia, Guangming Xing, Houduo Qi, Qi Li

      Pages 129-144

4. Email Surveillance and Filtering

   1. Discussion Tracking in Enron Email Using PARAFAC

      • Brett W. Bader, Michael W. Berry, Murray Browne

      Pages 147-163

   2. Spam Filtering Based on Latent Semantic Indexing

      • Wilfried N. Gansterer, Andreas G. K. Janecek, Robert Neumayer

      Pages 165-183

5. Anomaly Detection

   1. A Probabilistic Model for Fast and Confident Categorization of Textual Documents

      • Cyril Goutte

      Pages 187-202

   2. Anomaly Detection Using Nonnegative Matrix Factorization

      • Edward G. Allan, Michael R. Horvath, Christopher V. Kopek, Brian T. Lamb, Thomas S. Whaples, Michael W. Berry

      Pages 203-217

   3. Document Representation and Quality of Text: An Analysis

      • Mostafa Keikha, Narjes Sharif Razavian, Farhad Oroumchian, Hassan Seyed Razi

      Pages 219-232

6. Back Matter

   Pages 233-240

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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.

• Anomaly Detection
• Augmented Reality
• Classification
• Document Clustering
• Extensible Markup Language (XML)
• Information Extraction
• Information Retrieval
• Text Mining
• Topic Evolution
• algorithms

• Department of Computer Science, University of Tennessee, USA

  Michael W. Berry

• Hewlett-Packard Laboratories, Palo Alto, USA

  Malu Castellanos

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