Advances in Web Mining and Web Usage Analysis

8th International Workshop on Knowledge Discovery on the Web, WebKDD 2006 Philadelphia, USA, August 20, 2006 Revised Papers

  • Editors
  • Olfa Nasraoui
  • Myra Spiliopoulou
  • Jaideep Srivastava
  • Bamshad Mobasher
  • Brij Masand
Conference proceedings WebKDD 2006

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4811)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 4811)

Table of contents

  1. Front Matter
  2. Justin Brickell, Inderjit S. Dhillon, Dharmendra S. Modha
    Pages 1-20
  3. Kalyan Beemanapalli, Ramya Rangarajan, Jaideep Srivastava
    Pages 21-35
  4. Panagiotis Symeonidis, Alexandros Nanopoulos, Apostolos Papadopoulos, Yannis Manolopoulos
    Pages 36-55
  5. Alex Markov, Mark Last, Abraham Kandel
    Pages 56-71
  6. Bernard J. Jansen, Amanda Spink, Vinish Kathuria
    Pages 92-109
  7. Amit Bose, Kalyan Beemanapalli, Jaideep Srivastava, Sigal Sahar
    Pages 110-126
  8. Augusto Pucci, Marco Gori, Marco Maggini
    Pages 127-146
  9. Al Mamunur Rashid, Shyong K. Lam, Adam LaPitz, George Karypis, John Riedl
    Pages 147-166
  10. Chad A. Williams, Bamshad Mobasher, Robin Burke, Runa Bhaumik
    Pages 167-186
  11. David Nettleton, Liliana Calderón-Benavides, Ricardo Baeza-Yates
    Pages 207-226
  12. Ricardo Baeza-Yates, Álvaro Pereira, Nivio Ziviani
    Pages 227-246
  13. Back Matter

About these proceedings

Introduction

This book contains the postworkshop proceedings with selected revised papers from the 8th international workshop on knowledge discovery from the Web, WEBKDD 2006. The WEBKDD workshop series has taken place as part of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) since 1999. The discipline of data mining delivers methodologies and tools for the an- ysis of large data volumes and the extraction of comprehensible and non-trivial insights from them. Web mining, a much younger discipline, concentrates on the analysisofdata pertinentto the Web.Web mining methods areappliedonusage data and Web site content; they strive to improve our understanding of how the Web is used, to enhance usability and to promote mutual satisfaction between e-business venues and their potential customers. Inthelastfewyears,theinterestfortheWebasamediumforcommunication, interaction and business has led to new challenges and to intensive, dedicated research.Many ofthe infancy problems in Web mining have been solvedby now, but the tremendous potential for new and improved uses, as well as misuses, of the Web are leading to new challenges. ThethemeoftheWebKDD2006workshopwas“KnowledgeDiscoveryonthe Web”, encompassing lessons learned over the past few years and new challenges for the years to come. While some of the infancy problems of Web analysis have beensolvedandproposedmethodologieshavereachedmaturity,therealityposes newchallenges:TheWebisevolvingconstantly;siteschangeanduserpreferences drift. And, most of all, a Web site is more than a see-and-click medium; it is a venue where a user interacts with a site owner or with other users, where group behavior is exhibited, communities are formed and experiences are shared.

Keywords

DOM Session algorithms association rule mining classification clustering data mining filtering graph mining learning machine learning pattern mining recommender systems security user profiling

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-77485-3
  • Copyright Information Springer-Verlag Berlin Heidelberg 2007
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-77484-6
  • Online ISBN 978-3-540-77485-3
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book