Fundamentals of Predictive Text Mining

  • Sholom M. Weiss
  • Nitin Indurkhya
  • Tong Zhang

Part of the Texts in Computer Science book series (TCS)

Table of contents

  1. Front Matter
    Pages I-XIII
  2. Sholom M. Weiss, Nitin Indurkhya, Tong Zhang
    Pages 1-12
  3. Sholom M. Weiss, Nitin Indurkhya, Tong Zhang
    Pages 13-38
  4. Sholom M. Weiss, Nitin Indurkhya, Tong Zhang
    Pages 39-73
  5. Sholom M. Weiss, Nitin Indurkhya, Tong Zhang
    Pages 75-90
  6. Sholom M. Weiss, Nitin Indurkhya, Tong Zhang
    Pages 91-112
  7. Sholom M. Weiss, Nitin Indurkhya, Tong Zhang
    Pages 113-139
  8. Sholom M. Weiss, Nitin Indurkhya, Tong Zhang
    Pages 141-155
  9. Sholom M. Weiss, Nitin Indurkhya, Tong Zhang
    Pages 157-188
  10. Sholom M. Weiss, Nitin Indurkhya, Tong Zhang
    Pages 189-205
  11. Back Matter
    Pages 207-226

About this book


One consequence of the pervasive use of computers is that most documents originate in digital form. Widespread use of the Internet makes them readily available. Text mining - the process of analyzing unstructured natural-language text – is concerned with how to extract information from these documents.

Developed from the authors' highly successful Springer reference on text mining, Fundamentals of Predictive Text Mining is an introductory textbook and guide to this rapidly evolving field. Integrating topics spanning the varied disciplines of data mining, machine learning, databases, and computational linguistics, this uniquely useful book also provides practical advice for text mining. In-depth discussions are presented on issues of document classification, information retrieval, clustering and organizing documents, information extraction, web-based data-sourcing, and prediction and evaluation. Background on data mining is beneficial, but not essential. Where advanced concepts are discussed that require mathematical maturity for a proper understanding, intuitive explanations are also provided for less advanced readers.

Topics and features:

  • Presents a comprehensive, practical and easy-to-read introduction to text mining
  • Includes chapter summaries, useful historical and bibliographic remarks, and classroom-tested exercises for each chapter
  • Explores the application and utility of each method, as well as the optimum techniques for specific scenarios
  • Provides several descriptive case studies that take readers from problem description to systems deployment in the real world
  • Includes access to industrial-strength text-mining software that runs on any computer.
  • Describes methods that rely on basic statistical techniques, thus allowing for relevance to all languages (not just English)
  • Contains links to free downloadable software and other supplementary instruction material

Fundamentals of Predictive Text Mining is an essential resource for IT professionals and managers, as well as a key text for advanced undergraduate computer science students and beginning graduate students.

Dr. Sholom M. Weiss is a Research Staff Member with the IBM Predictive Modeling group, in Yorktown Heights, New York, and Professor Emeritus of Computer Science at Rutgers University. Dr. Nitin Indurkhya is Professor at the School of Computer Science and Engineering, University of New South Wales, Australia, as well as founder and president of data-mining consulting company Data-Miner Pty Ltd. Dr. Tong Zhang is Associate Professor at the Department of Statistics and Biostatistics at Rutgers University, New Jersey.


Active Learning Document Classification and Correction Extraction Retrieval Summarization classification clustering computer science data mining database information retrieval machine learning search engine marketing (SEM) statistics text mi

Authors and affiliations

  • Sholom M. Weiss
    • 1
  • Nitin Indurkhya
    • 2
  • Tong Zhang
    • 3
  1. 1.T.J. Watson Research CenterIBM CorporationYorktown HeightsUSA
  2. 2.School of Computer Science &, EngineeringUniversity of New South WalesSydneyAustralia
  3. 3.Dept. StatisticsRutgers UniversityPiscatawayUSA

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag London Limited 2010
  • Publisher Name Springer, London
  • eBook Packages Computer Science Computer Science (R0)
  • Print ISBN 978-1-84996-225-4
  • Online ISBN 978-1-84996-226-1
  • Series Print ISSN 1868-0941
  • Series Online ISSN 1868-095X
  • Buy this book on publisher's site