Text Mining: Predictive Methods for Analyzing Unstructured Information Softcover reprint of hardcover 1st ed. 2005 Edition

4 customer reviews
ISBN-13: 978-1441929969
ISBN-10: 1441929967
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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.

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Product Details

  • Paperback: 237 pages
  • Publisher: Springer; Softcover reprint of hardcover 1st ed. 2005 edition (November 19, 2010)
  • Language: English
  • ISBN-10: 1441929967
  • ISBN-13: 978-1441929969
  • Product Dimensions: 6 x 0.6 x 9 inches
  • Shipping Weight: 15.2 ounces (View shipping rates and policies)
  • Average Customer Review: 3.8 out of 5 stars  See all reviews (4 customer reviews)
  • Amazon Best Sellers Rank: #2,774,660 in Books (See Top 100 in Books)

Customer Reviews

Most Helpful Customer Reviews

21 of 28 people found the following review helpful By Zhefu Zhang on April 5, 2007
Format: 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.
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13 of 17 people found the following review helpful By Michael Stigall on July 7, 2005
Format: 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.
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3 of 5 people found the following review helpful By Y. Liu on December 28, 2006
Format: Hardcover
This is a great book on text mining. It provides every detail you need to know to build a search engine or text analysis. There are several other books available on similar topics, but this one is definitely the best. Among all the chapters in this book, I like chapter 2 the best. It provides a complete list of solutions to convert the unstructured texts into vectors. Many researchers and enginners are familiar with the process, but few pay attention to many aspects as the book did, such as sentence boundary determination and phrase recognition.

In one word, it is a great introduction book for someone new to the area, also a good handbook to check from time to time.
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5 of 10 people found the following review helpful By Demo Monkey on February 11, 2006
Format: Hardcover
I wouldn't give it 5 stars, but definitely worth the money. I took an online class at statistics.com that used this as the text. Really a great combination of book and class and wasn't expensive. Highly recommend both to any data miner interested in getting into text mining.
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