Mining the Web

Informationen zum Autor 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. Klappentext This is one of the first books devoted to issues and solutions related to hypertext data, and to developing the systems that will allow for easier information access of Web based information. Zusammenfassung Examines low-level machine learning techniques as they relate specifically to the challenges of Web mining. This work focuses on applications that unite infrastructure and analysis to bring machine learning to bear on systematically acquired and stored data. Inhaltsverzeichnis 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.

Autorentext
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.

Klappentext

This is one of the first books devoted to issues and solutions related to hypertext data, and to developing the systems that will allow for easier information access of Web based information.

Zusammenfassung
Examines low-level machine learning techniques as they relate specifically to the challenges of Web mining. This work focuses on applications that unite infrastructure and analysis to bring machine learning to bear on systematically acquired and stored data.

Inhalt

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.