Survey of Text Mining

Clustering, Classification, and Retrieval

  • Michael W. Berry

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Clustering and Classification

    1. Front Matter
      Pages 1-1
    2. Pierre P. Senellart, Vincent D. Blondel
      Pages 25-43
    3. Inderjit Dhillon, Jacob Kogan, Charles Nicholas
      Pages 73-100
  3. Information Extraction and Retrieval

    1. Front Matter
      Pages 101-101
    2. Mei Kobayashi, Masaki Aono
      Pages 103-122
    3. Malú Castellanos
      Pages 123-157
    4. Michael Cornelson, Ed Greengrass, Robert L. Grossman, Ron Karidi, Daniel Shnidman
      Pages 159-169
  4. Trend Detection

    1. Front Matter
      Pages 171-171
    2. Peiling Wang, Jennifer Bownas, Michael W. Berry
      Pages 173-183
    3. 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.


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

Editors and affiliations

  • Michael W. Berry
    • 1
  1. 1.Department of Computer ScienceUniversity of TennesseeKnoxvilleUSA

Bibliographic information