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

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

by Sholom M. Weiss, Nitin Indurkhya, Tong Zhang, Fred Damerau
     
 

The growth of the web can be seen as an expanding public digital library collection. Online digital information extends far beyond the web and its publicly available information. Huge amounts of information are private and are of interest to local communities, such as the records of customers of a business.
This information is overwhelmingly text and has its… See more details below

Overview

The growth of the web can be seen as an expanding public digital library collection. Online digital information extends far beyond the web and its publicly available information. Huge amounts of information are private and are of interest to local communities, such as the records of customers of a business.
This information is overwhelmingly text and has its record-keeping purpose, but an automated analysis might be desirable to find patterns in the stored records. Analogous to this data mining is text mining, which also finds patterns and trends in information samples but which does so with far less structured—though with greater immediate utility for users—ingredients.
This book focuses on the concepts and methods needed to expand horizons beyond structured, numeric data to automated mining of text samples. It introduces the new world of text mining and examines proven methods for various critical text-mining tasks, such as automated document indexing and information retrieval and search. New research areas are explored, such as information extraction and document summarization, that rely on evolving text-mining techniques.

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Product Details

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

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Meet the Author

Table of Contents

1Overview of text mining1
2From textual information to numerical vectors15
3Using text for prediction47
4Information retrieval and text mining85
5Finding structure in a document collection103
6Looking for information in documents129
7Case studies157
8Emerging directions197
AppSoftware notes213

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