WEBKDD 2002 - Mining Web Data for Discovering Usage Patterns and Profiles

4th International Workshop, Edmonton, Canada, July 23, 2002. Revised Papers

  • Osmar R. Zaïane
  • Jaideep Srivastava
  • Myra Spiliopoulou
  • Brij Masand
Conference proceedings WebKDD 2002

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2703)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 2703)

Table of contents

  1. Front Matter
  2. Harshit S. Shah, Neeraj R. Joshi, Ashish Sureka, Peter R. Wurman
    Pages 17-34
  3. Birgit Hay, Geert Wets, Koen Vanhoof
    Pages 50-65
  4. Enrique Frías-Martínez, Vijay Karamcheti
    Pages 66-85
  5. André Bergholz
    Pages 86-99
  6. Hui Yang, Srinivasan Parthasarathy
    Pages 100-118
  7. Shigeru Oyanagi, Kazuto Kubota, Akihiko Nakase
    Pages 119-136
  8. Andreas Geyer-Schulz, Michael Hahsler
    Pages 137-158
  9. Bettina Berendt, Bamshad Mobasher, Miki Nakagawa, Myra Spiliopoulou
    Pages 159-179
  10. Back Matter

About these proceedings


1 WorkshopTheme Data mining as a discipline aims to relate the analysis of large amounts of user data to shed light on key business questions. Web usage mining in particular, a relatively young discipline, investigates methodologies and techniques that - dress the unique challenges of discovering insights from Web usage data, aiming toevaluateWebusability,understandtheinterestsandexpectationsofusersand assess the e?ectiveness of content delivery. The maturing and expanding Web presents a key driving force in the rapid growth of electronic commerce and a new channel for content providers. Customized o?ers and content, made possible by discovered knowledge about the customer, are fundamental for the establi- ment of viable e-commerce solutions and sustained and e?ective content delivery in noncommercial domains. Rich Web logs provide companies with data about their online visitors and prospective customers, allowing microsegmentation and personalized interactions. While Web mining as a domain is several years old, the challenges that characterize data analysis in this area continue to be formidable. Though p- processing data routinely takes up a major part of the e?ort in data mining, Web usage data presents further challenges based on the di?culties of assigning data streams to unique users and tracking them over time. New innovations are required to reliably reconstruct sessions, to ascertain similarity and di?erences between sessions, and to be able to segment online users into relevant groups.


Alignment Session WWW algorithms association rule mining clustering data mining knowledge discovery recommender system usage analysis usage pattern discovery user profiling web access sequence mining web data mining web traffic mining

Editors and affiliations

  • Osmar R. Zaïane
    • 1
  • Jaideep Srivastava
    • 2
  • Myra Spiliopoulou
    • 3
  • Brij Masand
    • 4
  1. 1.University of AlbertaCanada
  2. 2.University of MinnesotaMinneapolisUSA
  3. 3.Faculty of Computer ScienceOtto-von-Guericke-University MagdeburgGermany
  4. 4.Data Miners Inc.BostonUSA

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2003
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-20304-9
  • Online ISBN 978-3-540-39663-5
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site
Industry Sectors
IT & Software