Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage [Hardcover]

Zdravko Markov (Author), Daniel T. Larose (Author)

Book Description

Publication Date: April 25, 2007 | ISBN-10: 0471666556 | ISBN-13: 978-0471666554 | Edition: 1

This book introduces the reader to methods of data mining on the web, including uncovering patterns in web content (classification, clustering, language processing), structure (graphs, hubs, metrics), and usage (modeling, sequence analysis, performance).

Editorial Reviews

Review

"…it has to be noted that this book is an excellent resource for conducting Web mining lectures or single units within Data mining class. The data can be used for small as well as quite comprehensive business intelligence projects. The book's content is easy to access; even students with very basic statistical skills can get the flavor of the intriguing aspects of Web mining." (Journal of Statistical Software, April 2008)

"…highlight[s] the exciting research related to data mining the Web…a detailed summary of the current state of the art." (CHOICE, December 2007)

"I can say I really enjoyed reading this book…a great educational resource for students and teachers." (Information Retrieval, 2008)

From the Back Cover

Learn How To Convert Web Data Into Web Knowledge

This text demonstrates how to extract knowledge by finding meaningful connections among data spread throughout the Web. Readers learn methods and algorithms from the fields of information retrieval, machine learning, and data mining which, when combined, provide a solid framework for mining the Web. The authors walk readers through the algorithms with the aid of examples and exercises.

This text is divided into three parts:

• Part One, Web Structure, presents basic concepts and techniques for extracting information from the Web. Readers learn how to collect and index Web documents as well as search and rank Web pages according to their textual content and hyperlink structure.

• Part Two, Web Content Management, offers two approaches, clustering and classification, for organizing Web content. For both approaches, the authors set forth specific algorithms that enable readers to convert Web data into knowledge.

• Part Three, Web Usage Mining, demonstrates the application of data mining methods to uncover meaningful patterns of Internet usage.

Methods and algorithms are illustrated by simple examples. More than 100 exercises help readers assess their grasp of the material. Further, thirty-four hands-on analysis problems ask readers to use their new data mining expertise to solve real problems, working with large data sets. All the data sets needed for the examples, exercises, and analysis problems are available on the companion Web site.

The extensive use of examples, along with the opportunity to test and apply data mining skills, makes this text ideal for graduate and upper-level undergraduates in computer science and engineering. Web designers and researchers will find that this text gives them a new set of tools to further mine the Web for knowledge and move well beyond the capabilities of standard search engines.

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Product Details

• Hardcover: 218 pages
• Publisher: Wiley-Interscience; 1 edition (April 25, 2007)
• Language: English
• ISBN-10: 0471666556
• ISBN-13: 978-0471666554
• Product Dimensions: 9.3 x 6.5 x 0.8 inches
• Shipping Weight: 1.2 pounds (View shipping rates and policies)
• Average Customer Review: 2.0 out of 5 stars  See all reviews (1 customer review)
• Amazon Best Sellers Rank: #1,096,531 in Books (See Top 100 in Books)

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Most Helpful Customer Reviews

5 of 5 people found the following review helpful:

2.0 out of 5 stars Absences disappoint reader of the three book series, March 29, 2008

By 

Keith McCormick (North Carolina, USA) - See all my reviews
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This review is from: Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage (Hardcover)

I really wanted to like this book. I am an almost daily user of Clementine, which is featured in this book series. Also, while giving them mixed reviews, I like aspects of the first two books in the series. Most of all, Web Mining is an application area I would like to know more about. The two other books are Discovering Knowledge in Data: An Introduction to Data Mining and Data Mining Methods and Models

My review of this book will be more brief than my reviews of the other two because my main complaint is the topics that are absent. This book really assumes that you have at least the first book in the series. The modeling section is a mere 20 pages and cites the other books often. This is not merely an annoying detail. It prevents the authors from cleaning, preparing, and modeling a thorough and complete case study from start to finish. They have a good case study in the book, but there is much left to the imagination because modeling has already been covered in the other books. For instance, the section entitled "The application of the A priori algorithm to the web log data" is three pages.

Since I buy several related books a year, I got this largely based on my knowledge of the first book. It is not that it is badly written. (However, readers of the series will be able to tell which co-author wrote which section. The styles differ.) Rather, it is that it doesn't address topics that I was certain would be included regarding Clementine. I expected Text Mining and Sequence Analysis to make an appearance and they are not mentioned. For instance, what about text mining blogs? Actually, unlike the other two books, Clementine does not make a very frequent appearance in this book, probably around 15% of the material. Obviously, explicit references to Clementine are useful to me. If they are to you, you might be disappointed.

Unlike the other two books, some of the material in this book was new to me as I read it. I found it more difficult to follow. Is that because I have more experience with the topics in the other two books? Perhaps. I just can't recommend this to someone unless it is going to be part of a large collection on the subject.