Mining the Web: Discovering Knowledge from Hypertext Data

Probably not a book you're going to put on your coffee table, but if you've got any interest in this subject matter at all this is a book worth having. You can flip it open to just about any page and find something interesting. Most of the descriptions in this book move from general to specific, so you can jump around from chapter to chapter getting an overview, or dig more deeply when you want more detail. The references at the end of each chapter are also very useful. Whether you want a survey of the field or are trying to implement something specific, this book is a valuable resource.

very interesting book.. data mining is one of the most interesting fields in computer science and this book covers very interesting parts I enjoyed reading it

The field of relevance algorithms for the web is still relatively new and the author provides a clear, informative introduction to the still-developing field. Many references to real problems are discussed, and the author avoids needless use of equations or symbolic logic when a simple textual explanation is more appropriate. This is the book that the authors of "Modelling the Internet and the Web" should have written. Avoid that book, it is a confusing disaster.

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]

This book is an excellent introduction to a number of techniques in information retrieval, machine learning, data mining, network analysis and the application of such techniques to the Web. It discusses many research issues as well as provides practical insights into constructing Web mining tools and systems. Chakrabarti has brought the wisdom of researchers in the area of Web mining to a wider audience. I think the book will prompt the development of new courses for graduate as well as senior undergraduate students. The first part of the book deals with interesting practical and theoretical issues related with designing large-scale Web crawlers and search engines. Chapter 4 and 5 are a good introduction to various unsupervised and supervised learning methods. Although proper understanding of advanced methods like the LSI are possible only through adequate foundation in linear algebra (you can get only a flavor of the technique in the book). Part III of the book is my personal favorite. It has detailed description of various social network analysis methods, some of which have been applied by modern search engines like Google. Focused crawling, an area that the author has personally shaped, is also explained well. The book ends with a brief peek into the future of Web mining. The comprehensive yet easy to read nature of the book makes it a valuable addition to my shelf. It is hard to find a comparable book in the area of Web mining.

I still gave it 5 stars though the effective page number is 326. There are mainly 3 sections in the book --- the first section is 79 pages walks you thru the basic structure of a web search engine, the 2nd one talks about the learning process (clustering, classification and so on), yes, I know it is AI related stuffs, but this book does not have too much equation and is quite readable. From page 203 is the 3rd section --- application which includes page ranking and other interesting topics.

The book is an absolute must for those working in information retrieval, and in particular web information retrieval and web mining. These areas are quite hot (again) both for the academics as well as for industry. I personally enjoyed the fact that there is no discussion of semantic web research directions (Jena, OWL etc.) but others might not... The material is quite tightly brought together and very comprehensibly written. However, especially in chapters 4 and 5 there are many pages containing mathematical errors (either in the formulas or in the algorithms described.) For this reason, I rate an otherwise excellent textbook with 4 stars.