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Text Mining

Predictive Methods for Analyzing Unstructured Information

Sholom M. Weiss and Others

This book is available for download with iBooks on your Mac or iOS device, and with iTunes on your computer. Books can be read with iBooks on your Mac or iOS device.

Description

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.

Text Mining
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  • 79,99 €
  • Available on iPhone, iPad, iPod touch, and Mac.
  • Category: Databases
  • Published: 08 January 2010
  • Publisher: Springer New York
  • Print Length: 248 Pages
  • Language: English
  • Requirements: To view this book, you must have an iOS device with iBooks 1.3.1 or later and iOS 4.3.3 or later, or a Mac with iBooks 1.0 or later and OS X 10.9 or later.

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