Mining the Web

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Product Description

Mining the Web: Discovering Knowledge from Hypertext Data is the first book devoted entirely to techniques for producing knowledge from the vast body of unstructured Web data. Building on an initial survey of infrastructural issuesâ �� ”including Web crawling and indexing� ¢ €Â”Chakrabarti examines low-level machine learning techniques as they relate specifically to the challenges of Web mining. He then devotes the final part of the book to applications that unite infrastructure and analysis to bring machine learning to bear on systematically acquired and stored data. Here the focus is on results: the strengths and weaknesses of these applications, along with their potential as foundations for further progress. From Chakrabarti's workâ � Â� �painstaking, critical, and forward-looking� ¢ €� ”readers will gain the theoretical and practical understanding they need to contribute to the Web mining effort.* A comprehensive, critical exploration of statistics-based attempts to make sense of Web Mining.* Details the special challenges associated with analyzing unstructured and semi-structured data.* Looks at how classical Information Retrieval techniques have been modified for use with Web data.* Focuses on today's dominant learning methods: clustering and classification, hyperlink analysis, and supervised and semi-supervised learning.* Analyzes current applications for resource discovery and social network analysis.* An excellent way to introduce students to especially vital applications of data mining and machine learning technology.

Mining the Web Review

Executive 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.
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