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 issuesincluding Web crawling and indexingChakrabarti 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 workpainstaking, critical, and forward-lookingreaders will gain the theoretical and practical understanding they need to contribute to the Web mining effort. Editorial Reviews 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 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 Review The definitive book on mining the Web from the preeminent authority. Book Description The definitive book on mining the Web from the preeminent authority. From the Back Cover Mining the Web: Discovering Knowledge from Hypertext Data is the first book devoted entirely to techniques for extracting and producing knowledge from the vast body of unstructured Web data. Building on an initial survey of infrastructural issuesincluding Web crawling and indexingChakrabarti examines machine learning techniques as they relate specifically to the challenges of Web mining and provides applications of machine learning to sytematically acquire, store, and analyze data. Here the focus is on results: the strengths and weaknesses of these applications, along with their potential as foundations for further progress toward a Web that is more aware of content semantics. This thorough and forward-looking book gives the theoretical and practical foundations you need to build innovative applications for mining the Web. Features A comprehensive, critical exploration of statistics-based attempts to make sense of Web data. 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. About the Author Soumen Chakrabarti is assistant Professor in Computer Science and Engineering at the Indian Institute of Technology, Bombay. Prior to joining IIT, he worked on hypertext databases and data mining at IBM Almaden Research Center. He has developed three systems and holds five patents in this area. Chakrabarti has served as a vice-chair and program committee member for many conferences, including WWW, SIGIR, ICDE, and KDD, and as a guest editor of the IEEE TKDE special issue on mining and searching the Web. His work on focused crawling received the Best Paper award at the 8th International World Wide Web Conference (1999). He holds a Ph.D. from the University of California, Berkeley.
