Mining the Web: Discovering Knowledge from Hypertext Data / Edition 1

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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.

Audience: Data mining academics, research and development professionals in data mining, senior/graduate level students in computer science.

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Editorial Reviews

From the Publisher
"...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.

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Product Details

Meet 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.

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Table of Contents

Preface. Introduction. I Infrastructure: Crawling the Web. Web search. II Learning: Similarity and clustering. Supervised learning for text. Semi-supervised learning. III Applications: Social network analysis. Resource discovery. The future of Web mining.

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  • Anonymous

    Posted December 2, 2002

    Comprehensive description of web mining techniques

    This book describes a range of techniques drawn from academia and experience for discovering resources by crawling the web, and for cluster, classifying and otherwise analyzing these resources. --- Part I is a practical discussion of the infrastructure of search engines, including large-scale web crawlers and information retrieval techniques. Although many existing crawlers originated in universities where they were the subject of academic description, the rise of commercial search engines means that more recent developments are not widely known. Part II discusses a broad range of unsupervised, supervised, and semi-supervised clustering and classification techniques, and Part III explores some applications and outcomes of the ideas developed in the previous sections. In several interesting cases, these applications and outcomes are compared to the (known or suspected) practices of commercial search engines. Having said that, the book emphasizes techniques that do not require that the entire web be mined, only the most relevant parts. --- After the first chapter, most of the material is academic in nature. Topics are described to the level of pseudocode and formulae (with many references to more comprehensive coverage); and the advantages and disadvantages of competing approaches are described, and in many cases demonstrated with practical evaluations. This level of detail is appropriate because the vast size of the web demands that programs be computationally efficient. --- In general, the writing is articulate, the explanations are clear, and the material well-researched. I found the book most valuable for the cohesive overview it provides; its insights into the operation of both large and small-scale crawlers; and its exploration of classification systems that combine the purely textual features of traditional Information Retrieval with link-based features from hypertext graph analysis. I expect that readers with little background in crawling and classification who intend to start Mining the Web will find this an excellent guide; and that experts and academics will find that the author pulls together, discusses and contrasts so many areas of interest, in such detail, as to form a valuable reference.

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