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  • © 2004

Survey of Text Mining

Clustering, Classification, and Retrieval

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  • ISBN: 978-1-4757-4305-0
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Softcover Book USD 159.00
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Table of contents (9 chapters)

  1. Front Matter

    Pages i-xvii
  2. Clustering and Classification

    1. Front Matter

      Pages 1-1
    2. Automatic Discovery of Similar Words

      • Pierre P. Senellart, Vincent D. Blondel
      Pages 25-43
    3. Feature Selection and Document Clustering

      • Inderjit Dhillon, Jacob Kogan, Charles Nicholas
      Pages 73-100
  3. Information Extraction and Retrieval

    1. Front Matter

      Pages 101-101
    2. Vector Space Models for Search and Cluster Mining

      • Mei Kobayashi, Masaki Aono
      Pages 103-122
    3. HotMiner: Discovering Hot Topics from Dirty Text

      • MalĂș Castellanos
      Pages 123-157
    4. Combining Families of Information Retrieval Algorithms Using Metalearning

      • Michael Cornelson, Ed Greengrass, Robert L. Grossman, Ron Karidi, Daniel Shnidman
      Pages 159-169
  4. Trend Detection

    1. Front Matter

      Pages 171-171
    2. Trend and Behavior Detection from Web Queries

      • Peiling Wang, Jennifer Bownas, Michael W. Berry
      Pages 173-183
    3. A Survey of Emerging Trend Detection in Textual Data Mining

      • April Kontostathis, Leon M. Galitsky, William M. Pottenger, Soma Roy, Daniel J. Phelps
      Pages 185-224
  5. Back Matter

    Pages 225-244

About this book

 

As the volume of digitized textual information continues to grow, so does the critical need for designing robust and scalable indexing and search strategies/software to meet a variety of user needs. Knowledge extraction or creation from text requires systematic, yet reliable processing that can be codified and adapted for changing needs and environments.

Survey of Text Mining is a comprehensive edited survey organized into three parts: Clustering and Classification; Information Extraction and Retrieval; and Trend Detection. Many of the chapters stress the practical application of software and algorithms for current and future needs in text mining. Authors from industry provide their perspectives on current approaches for large-scale text mining and obstacles that will guide R&D activity in this area for the next decade.

Topics and features:

* Highlights issues such as scalability, robustness, and software tools

* Brings together recent research and techniques from academia and industry

* Examines algorithmic advances in discriminant analysis, spectral clustering, trend detection, and synonym extraction

* Includes case studies in mining Web and customer-support logs for hot- topic extraction and query characterizations

* Extensive bibliography of all references, including websites

This useful survey volume taps the expertise of academicians and industry professionals to recommend practical approaches to purifying, indexing, and mining textual information. Researchers, practitioners, and professionals involved in information retrieval, computational statistics, and data mining, who need the latest text-mining methods and algorithms, will find the book an indispensable resource.

Keywords

  • algorithms
  • behavior
  • classification
  • clustering
  • data mining
  • information extraction
  • information retrieval
  • knowledge
  • search strategy
  • semantics
  • text mining

Editors and Affiliations

  • Department of Computer Science, University of Tennessee, Knoxville, USA

    Michael W. Berry

Bibliographic Information

Buying options

eBook USD 119.00
Price excludes VAT (USA)
  • ISBN: 978-1-4757-4305-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 159.00
Price excludes VAT (USA)
Hardcover Book USD 159.99
Price excludes VAT (USA)