Mining the Web: Discovering Knowledge from Hypertext Data Review

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Mining the Web: Discovering Knowledge from Hypertext Data ReviewExecutive summary: This is a fabulous book, written with care and
precision, easy to read yet covering in detail a wide variety of
the most beautiful and promising developments in data mining and
machine learning as it relates to the World Wide Web, including a
prescient vision of where the field is headed in the future.
More detail: There are science authors who are clear experts in
their field, yet have trouble communicating their knowledge. Then
there are science authors who write with clarity, but achieve it
by dumbing down technical details to cater to a broad readership.
Finally, there are authors who are experts and leaders in their
field, who are actively contributing to the forefront of research,
who are excellent writers, and who can communicate complex
concepts to a diverse audience with acumen, without glossing over
important details. Soumen Chakrabarti is one such author. "Mining
the Web" is a stunning achievement. It is an excellent summary of
the past decade or so of research in the area, covering nearly all
of the important bases, including the machinery of Web crawling,
Web information retrieval (i.e., search engines), clustering,
automated classification, semi-supervised approaches, social
network analysis, and focused crawling. Though Chakrabarti himself
has contributed prominently to the field, this book is not at all
the vehicle for self-promotion that other specialist texts
sometimes feel like. The book should be valuable to newcomers,
students, and experts alike, and could certainly serve as an
excellent course textbook. High-level concepts can be grasped with
little mathematical background, yet more technically sophisticated
readers will not be disappointed: most topics do include rigorous
coverage. The text is well organized, well written, and well
conceived. It's design, including generous and illuminating
figures and illustrations, possesses an artist's touch, perhaps
not surprising given that Chakrabarti designs his own font
libraries in his (apparently scant) spare time. It's hard to
imagine where Chakrabarti found the time to write such a
comprehensive and thoughtful book, but I'm not asking any
questions: I'm thrilled with the outcome. The book is a must-have
reference for anyone working in -- or aspiring to work in -- the
crossroads of Web algorithmics, data mining, and machine learning.
David M. Pennock
Senior Research Scientist, Overture Services, Inc.
[website]Mining the Web: Discovering Knowledge from Hypertext Data Overview

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Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications) Review

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Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications) ReviewSo what does the author, Bing Liu know about Web data mining to write the book "Web Data Mining - Exploring Hyperlinks, Contents, and Usage Data"[1] ? Fortunately the answer is "a lot!" This fact along with the title which had some cosine similarity with the names of my research lab and a graduate course that I have been teaching at the University of Louisville since 2004, and prior to that at the University of Memphis since 2000, are the reasons why I ordered a copy of this book. Bing Liu is a well seasoned researcher who has made significant contributions to association rule mining, in particular classification using association rule mining and association rule mining with multiple supports. He has also worked on Web data extraction, and more recently on opinion mining. In addition to the expertise of the author, two of the chapters, Chapter 8, Web Crawling, and Chapter 12, Web Usage Mining, were contributed by two leading experts in these respective areas, Filippo Menczer for the former and Bamshad Mobasher for the latter.
This book is appropriate for students at the graduate or senior undergraduate level, for practitioners in industry, and even as a good comprehensive reference for researchers in academia.
The Table of Contents held a surprise for someone who had always found it hard to limit the number of textbooks to one book in a web mining course that does not have data mining as prerequisite, and thus typically prescribes a good data mining book to introduce data mining techniques, in addition to a second book related to web mining. This book, on the other hand, has two parts, one devoted to data mining, and the other devoted to Web mining. While it was not a problem to find a very good data mining book (I have a few of them on my bookshelf), it was harder to find a book that addressed data mining and Web mining. It was also hard to find a good and comprehensive Web mining book, since most of them tend to focus on one or only two of the three main Web mining areas of Web structure, content, and usage mining (typically leaving Web usage mining in the dark, with just a small section, citing that it is an emerging area). This book, on the other hand, is a serious book on Web mining that also devotes a decent portion to data mining. I would describe the way the topics are presented as deep and rigorous enough in most chapters, which is in contrast to a large number of books on data mining and web mining. That said, because the book is full of simple examples that illustrate the methods being discussed, it is useful even for beginners, making it also appropriate for an introductory level course.
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