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.
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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.
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Hardcover. Etat : Very Good. 368 pages. Mining the Web: Discovering Knowledge from Hypertext Data is the first book d evoted entirely to techniques for producing knowledge from the vast body of unst ructured Web data. Building on an initial survey of infrastructural issuesinclud ing Web crawling and indexingChakrabarti examines low-level machine learning tec hniques as they relate specifically to the challenges of Web mining. He then dev otes the final part of the book to applications that unite infrastructure and an alysis to bring machine learning to bear on systematically acquired and stored d ata. Here the focus is on results: the strengths and weaknesses of these applica tions, along with their potential as foundations for further progress. From Chak rabarti's workpainstaking, critical, and forward-lookingreaders will gain the th eoretical 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 i mplementation. = Bernard J. Jansen, Information Processing & Management The treatment is systematic, comprehensive and in-depth, yet very lucid and accessi ble to a wide range of Web technology developers. The author's insights and dept h of knowledge as on of the pioneering researchers on hypertext information mini ng and retrieval are also evident in the extensive and useful bibliographic note s provided at the end of each chapter. - Professor Joydeep Ghosh, University o f Texas, Austin The author has done the community a great service by synthesizi ng 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 firs. N° de réf. du vendeur 339a
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