Mining the Web: Discovering Knowledge from Hypertext Data

Features

• Cover Type: Hard Cover with 344 pages
• Published by: Morgan Kaufmann
• Edition: 1st Edition August 15, 2002
• Written in: English
• ISBN 10 Number: 1558607544
• ISBN 13 Number: 978-1558607545
• Book Dimensions: 9.7 x 7.6 x 1 inches
• Weighs: 1.4 pounds

Product Review
"This book, for the first time, makes it possible to offer Web Mining as a real course." -- Professor Jaideep Srivastava, University of Minnesota. -- Review

Product Review
"solid and beneficial to readers interested in Web data mining, especially those interested in the details of algorithmic implementation." - Bernard J. Jansen, Information Processing & Management

"This book, for the first time, makes it possible to offer Web Mining as a real course." -- Professor Jaideep Srivastava, University of Minnesota.

Reader Reviews
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. [website]