Text Mining: Predictive Methods for Analyzing Unstructured Information / Edition 1

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Overview

"Text Mining presents a comprehensive introduction and overview of the field, integrating related topics (such as artificial intelligence and knowledge discovery and data mining) and providing practical advice on how readers can use text-mining methods to analyze their own data. Emphasizing predictive methods, the book unifies all key areas in text mining: preprocessing, text categorization, information search and retrieval, clustering of documents, and information extraction. In addition, it identifies emerging directions for those looking to do research in the area. Some background in data mining is beneficial, but not essential." This authoritative and highly accessible text, written by a team of authorities on text mining, develops the foundation concepts, principles, and methods needed to expand beyond structured, numeric data to automated mining of text samples. Researchers, professionals, and advanced undergraduates and graduates with work and interests in data mining, machine learning, databases, and computational linguistics will find the work an essential resource.
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Product Details

  • ISBN-13: 9780387954332
  • Publisher: Springer New York
  • Publication date: 10/25/2004
  • Edition description: 2005
  • Edition number: 1
  • Pages: 236
  • Product dimensions: 6.14 (w) x 9.21 (h) x 0.63 (d)

Table of Contents

1 Overview of text mining 1
2 From textual information to numerical vectors 15
3 Using text for prediction 47
4 Information retrieval and text mining 85
5 Finding structure in a document collection 103
6 Looking for information in documents 129
7 Case studies 157
8 Emerging directions 197
App Software notes 213
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