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Web Usage Analysis and User Profiling

International WEBKDD’99 Workshop San Diego, CA, USA, August 15, 1999 Revised Papers

  • Brij Masand
  • Myra Spiliopoulou
Conference proceedings WebKDD 1999

Part of the Lecture Notes in Computer Science book series (LNCS, volume 1836)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 1836)

Table of contents

  1. Front Matter
    Pages I-6
  2. Modelling the Users

    1. Yongjian Fu, Kanwalpreet Sandhu, Ming-Yi Shih
      Pages 21-38
    2. Alan J. Broder
      Pages 56-73
  3. Discovering Rules and Patterns of Navigation

    1. Matthias Baumgarten, Alex G. Büchner, Sarabjot S. Anand, Maurice D. Mulvenna, John G. Hughes
      Pages 74-91
    2. José Borges, Mark Levene
      Pages 92-112
    3. Bin Lan, Stéphane Bressan, Beng Chin Ooi, Y. C. Tay
      Pages 112-125
  4. Measuring Interestingness in Web Usage Mining

    1. Juhnyoung Lee, Mark Podlaseck, Edith Schonberg, Robert Hoch, Stephen Gomory
      Pages 126-141
    2. Myra Spiliopoulou, Carsten Pohle, Lukas C. Faulstich
      Pages 142-162
    3. Robert Cooley, Pang-Ning Tan, Jaideep Srivastava
      Pages 163-182
  5. Back Matter
    Pages 183-183

About these proceedings

Introduction

After the advent of data mining and its successful application on conventional data, Web-related information has been an appropriate and increasingly popular target of knowledge discovery. Depending on whether the data used in the knowledge discovery process concerns the Web itself in terms of content or the usage of the content, one distinguishes between Web content mining and Web usage mining.
This book is the first one entirely devoted to Web usage mining. It originates from the WEBKDD'99 Workshop held during the 1999 KDD Conference. The ten revised full papers presented together with an introductory survey by the volume editors documents the state of the art in this exciting new area. The book presents topical sections on Modeling the User, Discovering Rules and Patterns of Navigation, and Measuring interestingness in Web Usage Mining.

Keywords

Internet Data Mining Navigation Patterns User Modeling Web Web Site Personalization Web Usage Analysis Web Usage Mining Web User Profiling data mining knowledge knowledge discovery modeling navigation

Editors and affiliations

  • Brij Masand
    • 1
  • Myra Spiliopoulou
    • 2
  1. 1.Discovery and Intelligent Agents TechnologyRedwood Investment Systems Inc.BostonUSA
  2. 2.Institut für WirtschaftsinformatikHumboldt Universität zu BerlinBerlinGermany

Bibliographic information

  • DOI https://doi.org/10.1007/3-540-44934-5
  • Copyright Information Springer-Verlag Berlin Heidelberg 2000
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-67818-2
  • Online ISBN 978-3-540-44934-8
  • Series Print ISSN 0302-9743
  • Buy this book on publisher's site
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