
Text Mining
Predictive Methods for Analyzing Unstructured Information
Springer (Publisher)
Published on 19. November 2010
Book
Paperback/Softback
XII, 237 pages
978-1-4419-2996-9 (ISBN)
Description
Data mining is a mature technology. The prediction problem, looking for predictive patterns in data, has been widely studied. Strong me- ods are available to the practitioner. These methods process structured numerical information, where uniform measurements are taken over a sample of data. Text is often described as unstructured information. So, it would seem, text and numerical data are different, requiring different methods. Or are they? In our view, a prediction problem can be solved by the same methods, whether the data are structured - merical measurements or unstructured text. Text and documents can be transformed into measured values, such as the presence or absence of words, and the same methods that have proven successful for pred- tive data mining can be applied to text. Yet, there are key differences. Evaluation techniques must be adapted to the chronological order of publication and to alternative measures of error. Because the data are documents, more specialized analytical methods may be preferred for text. Moreover, the methods must be modi?ed to accommodate very high dimensions: tens of thousands of words and documents. Still, the central themes are similar.
More details
Edition
Softcover reprint of hardcover 1st ed. 2005
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Research
Illustrations
XII, 237 p.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 14 mm
Weight
388 gr
ISBN-13
978-1-4419-2996-9 (9781441929969)
DOI
10.1007/978-0-387-34555-0
Schweitzer Classification
Other editions
Additional editions

Sholom M. Weiss | Nitin Indurkhya | Tong Zhang
Text Mining
Predictive Methods for Analyzing Unstructured Information
Book
10/2004
Springer
€160.49
Shipment within 5-7 days
Persons
Dr. Sholom M. Weiss is a Professor Emeritus of Computer Science at Rutgers University, a Fellow of the Association for the Advancement of Artificial Intelligence, and co-founder of AI Data-Miner LLC, New York.
Dr. Nitin Indurkhya is faculty member at the School of Computer Science and Engineering, University of New South Wales, Australia, and the Institute of Statistical Education, Arlington, VA, USA. He is also a co-founder of AI Data-Miner LLC, New York.
Dr. Tong Zhang is a Professor of Statistics and Biostatistics at Rutgers University.
Content
Overview of Text Mining.- From Textual Information to Numerical Vectors.- Using Text for Prediction.- Information Retrieval and Text Mining.- Finding Structure in a Document Collection.- Looking for Information in Documents.- Case Studies.- Emerging Directions.