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Survey of Text Mining

Clustering, Classification, and Retrieval

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  • © 2004

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About this book

Extracting content from text continues to be an important research problem for information processing and management. Approaches to capture the semantics of text-based document collections may be based on Bayesian models, probability theory, vector space models, statistical models, or even graph theory.

As the volume of digitized textual media continues to grow, so does the need for designing robust, 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.

This book will draw upon experts in both academia and industry to recommend practical approaches to the purification, indexing, and mining of textual information. It will address document identification, clustering and categorizing documents, cleaning text, and visualizing semantic models of text.

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Table of contents (9 chapters)

  1. Clustering and Classification

  2. Information Extraction and Retrieval

  3. Trend Detection

Editors and Affiliations

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

    Michael W. Berry

Bibliographic Information

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