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Mining the Web: Discovering Knowledge from Hypertext Data (Anglais) Relié – 16 octobre 2002


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Revue de presse

"...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

"The treatment is systematic, comprehensive and in-depth, yet very lucid and accessible to a wide range of Web technology developers. The author's insights and depth of knowledge as on of the pioneering researchers on hypertext information mining and retrieval are also evident in the extensive and useful bibliographic notes provided at the end of each chapter..." - Professor Joydeep Ghosh, University of Texas, Austin

"The author has done the community a great service by synthesizing all the important work in this field into an excellent book, which introduces fairly sophisticated material in an easy-to-read manner. This book for the first time, makes it possible to offer Web Mining as a real course." - Professor Jaideep Srivastava, University of Minnesota

" Mining the Web: Discovering Knowledge from Hypertext from Hypertext Data, by Soumen Chakrabarti, focuses extensively on building a better search engine crawler...Chakrabarti's book begins with a discussion of search engine crawlers in a chapter titled "Crawling the Web." The discussion in this chapter is technical and detailed. Readers learn about features such as the robots.txt file that can be written in a certain way to stop crawlers from visiting a page...The most interesting part of the book is perhaps Chapter 7, "Social Network Analysis." In this chapter, the author presents the most famous search engine algorithms (e.g., PageRank, HITS, SALSA)." - Journal of Marketing Research, Sandeep Krishnamurthy

"All in all this is an excellent book. I enjoyed the book and highly recommend it as a textbook for web data mining classes at graduate or senior undergraduate levels. Chakrabarti has a rich vocabulary and is a gifted writer. I bet he will write new, good books in the future, and he should. I look forward to them." - Fazli Can - Miami University

Présentation de l'éditeur

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 issues―including Web crawling and indexing―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 work―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.


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Dans ce livre (En savoir plus)
Première phrase
The World Wide Web is the largest and most widely known repository of hypertext. Lire la première page
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Couverture | Copyright | Table des matières | Extrait | Index
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Amazon.com: 9 commentaires
46 internautes sur 47 ont trouvé ce commentaire utile 
Excellent, comprehensive, readable book on mining the Web 29 août 2003
Par Dave P - Publié sur Amazon.com
Format: Relié
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]
11 internautes sur 12 ont trouvé ce commentaire utile 
A wonderful textbook for machine learning over the web 8 septembre 2004
Par Ari Rappoport - Publié sur Amazon.com
Format: Relié
This book is one of the best computer science textbooks i have ever seen. Apart from the wealth of information and discussion on specific WEB crawling and data mining (chapters 2, 3, 7, 8), chapters 4, 5 and 6 constitute a wonderful summary of machine learning in general.

The book's discussion of unsupervised learning (the EM algorithm, advanced algorithms in which the number of clusters is not known in advance), supervised learning (Bayesian networks, entropian methods, SVMs), semisupervised learning, co-training and rule induction is extraordinary in that it is short, intuitive, does not sacrifice mathematical rigor, and accompanied by examples (all taken from information retreival over the web).
9 internautes sur 11 ont trouvé ce commentaire utile 
Great coverage, but quite a few errors 3 juin 2005
Par I. Christou - Publié sur Amazon.com
Format: Relié
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.
9 internautes sur 12 ont trouvé ce commentaire utile 
The Best Web Data Mining Text 3 juillet 2003
Par Dr. G. W. Flake - Publié sur Amazon.com
Format: Relié
This book is simply the best web data mining text available. It is simultaneously broad and deep, covering a wide array of topics yet delving into the meatiest parts of Web data mining. Topics covered include classic information retrieval, graph theoretic approaches, Web measurements, and even machine learning methods such as clustering and text classification. One of the reasons why the book succeeds is that Chakrabarti is himself a major contributor to the field. His writing is always clear and precise probably because he frequently lectures on these topics. If you buy one book about data mining on the Web, this should be that book.
13 internautes sur 18 ont trouvé ce commentaire utile 
Much needed book on Web mining 29 avril 2003
Par Gautam Pant - Publié sur Amazon.com
Format: Relié
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.
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