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Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage (Hardcover)

by Zdravko Markov (Author), Daniel T. Larose (Author)
Key Phrases: web usage miner, basket transformation, average time per page, Criminal Justice, Political Science, Modern Languages (more...)

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Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications) by Bing Liu
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The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data by Ronen Feldman
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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)

Product Description
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).

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Product Details

Hardcover: 218 pages
Publisher: Wiley-Interscience (April 25, 2007)
Language: English
ISBN-10: 0471666556
ISBN-13: 978-0471666554
Product Dimensions: 9.3 x 6.2 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.com Sales Rank: #752,066 in Books (See Bestsellers in Books)

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Inside This Book (learn more)

Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
web usage miner, basket transformation, average time per page, book series web site, web log data, duration bin, extended common log format, intracluster similarity, web log entries, cluster overlay, web log entry, user agent field, visit actions, priori algorithm, web log file, data mining the web, referrer field, rank sink, interpolated precision, web usage mining, topic directories, session actions, session duration, hub scores, cosine similarity

Key Phrases - Capitalized Phrases (CAPs): (learn more)
Criminal Justice, Political Science, Modern Languages, Data Mining the Web, John Wiley, Larose Copyright, Uncovering Patterns, Daniel Larose, Discovering Knowledge, San Francisco, Morgan Kaufmann, Ross Quinlan, Class Anthropology, Multipage Visits Mean, Consider Figure, History Music Philosophy

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What Do Customers Ultimately Buy After Viewing This Item?

	67% buy the item featured on this page: Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage 2.0 out of 5 stars (1) $63.16
	14% buy Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications) 4.3 out of 5 stars (3) $47.96
	12% buy Data Mining Methods and Models 3.0 out of 5 stars (1) $86.36
	8% buy Discovering Knowledge in Data: An Introduction to Data Mining 4.0 out of 5 stars (2) $74.80

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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 (REAL NAME)

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.

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