Text Mining: Predictive Methods for Analyzing Unstructured Information [Hardcover]

Sholom Weiss (Author), Nitin Indurkhya (Author), Tong Zhang (Author), Fred Damerau (Author)

Book Description

ISBN-10: 0387954333 | ISBN-13: 978-0387954332 | Publication Date: October 25, 2004 | Edition: 1

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.

Editorial Reviews

From the Back Cover

One consequence of the pervasive use of computers is that most documents originate in digital form. Text mining—the process of searching, retrieving, and analyzing unstructured, natural-language text—is concerned with how to exploit the textual data embedded in these documents. 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. Topics and features: * Presents a comprehensive and easy-to-read introduction to text mining * Explores the application and utility of the methods, as well as the optimal techniques for specific scenarios * Provides several descriptive case studies that take readers from problem description to system deployment in the real world * Uses methods that rely on basic statistical techniques, thus allowing for relevance to all languages (not just English) * Includes access to downloadable software (runs on any computer), as well as useful chapter-ending historical and bibliographical remarks, a detailed bibliography, and subject and author indexes 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, computer scientists, 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.

Product Details

• Hardcover: 248 pages
• Publisher: Springer; 1 edition (October 25, 2004)
• Language: English
• ISBN-10: 0387954333
• ISBN-13: 978-0387954332
• Product Dimensions: 9.3 x 6.1 x 0.7 inches
• Shipping Weight: 1.1 pounds (View shipping rates and policies)
• Average Customer Review: 4.0 out of 5 stars  See all reviews (5 customer reviews)
• Amazon Best Sellers Rank: #713,626 in Books (See Top 100 in Books)

Customer Reviews

Most Helpful Customer Reviews

20 of 26 people found the following review helpful:

1.0 out of 5 stars it is 200 pages thick, April 5, 2007

By 

Zhefu Zhang (USA) - See all my reviews
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This review is from: Text Mining: Predictive Methods for Analyzing Unstructured Information (Hardcover)

The authors (4 guys) tried to cover all IR big words as much as they can, and ended up with the 221 pages book. Let's take one example, inverted index takes 1.5 pages: It says inverted index is a table with the key-pair. The key is all the keywords scanned from the source, and the value is the document and word position (key in that document), period. IMHO, it is apparent facts that inverted index is like this way. But in practical world algorithm, it is much more complicated than a table, for example, how to incremental fill the index, how to sync between multiple backup copy, how to blabla. And even THAT google paper is more useful than it on this area. People may argue it is a comprehensive introduction book, well, then try Gerald's classic book. The whole impression is like I am reading a C++ programming book which spends 10 pages talking K&R's from Bell, how long they had been there, etc ...
I spent about 1 hr scanning the whole book without much left on my brain . Considering the price 69 bucks, I have to give it 0 on performance/price.

13 of 17 people found the following review helpful:

5.0 out of 5 stars An excellent introduction, July 7, 2005

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Michael Stigall "JavaCat" (West Virginia) - See all my reviews
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This review is from: Text Mining: Predictive Methods for Analyzing Unstructured Information (Hardcover)

I found the book informative and timely. The book describes the algorithms with psuedocode, and this made it possible for me to apply the algorithms to a legacy structure using another language within a few days of finishing the book.

The book's software (available from their website) requires XML formatted documents for input.

8 of 10 people found the following review helpful:

5.0 out of 5 stars an idiot savant, statistical viewpoint, October 26, 2006

By 

W Boudville (Terra, Sol 3) - See all my reviews
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This review is from: Text Mining: Predictive Methods for Analyzing Unstructured Information (Hardcover)

The authors give an excellent review of how matters stood in 2004, regarding text mining. The approach of the book is to minimise linguistic and semantic analysis. Instead, it looks more at the statistics of words (tokens) in documents. By using various such methods, they offer an automated way to classify documents. When this works, it can be a tremendous saver of manual effort. Think of the book as perhaps advocating an idiot savant vantage, and seeing how far one can usefully take this approach.

The results of the methods can also be used as input to more advanced and specialised methods, that rely on semantic analysis.

The book can also be applied to search engine analysis.