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This book constitutes the thoroughly refereed post-proceedings of the Third International Workshop on Mining Web Data, WEBKDD 2001 held in San Francisco, CA, USA in August 2001.

The seven revised full papers went through two rounds of reviewing an improvement. The book addresses key issues in mining Web log data for e-commerce. The papers are devoted to predicting user access, recommender systems and access modeling, and acquiring and modeling data and patterns.

Detail and Context in Web Usage Mining:Coarsening and Visualizing Sequences

Bettina Berendt

A Customer Purchase Incidence Model Applied to Recommender Services

Andreas Geyer-Schulz, Michael Hahsler, and MaximiUian Jahn

A Cube Model and Cluster Analysis for Web Access Sessions Zhexue Huang, Michael Ng, Wai-Ki Ching, Joe Ng,

and David Cheung

Exploiting Web Log Mining for Web Cache Enhancement

Alexandros Nanopoulos, Dimitrios Katsaros, and Yannis Manolopoulos

LOGML: Log Markup Language for Web Usage Mining

John R. Punin, Mukkai S. Krishnamoorthy, and Mohammed J. Zaki

A Framework for Efficient and Anonymous Web Usage Mining Based on Client-Side Tracking

Cyrus Shahabi and Farnoush Banaei-Kashani

Mining Indirect Associations in Web Data

Pang-Ning Tan and Vipin Kumar

Author Index

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This book constitutes the thoroughly refereed post-proceedings of the Third International Workshop on Mining Web Data, WEBKDD 2001 held in San Francisco, CA, USA in August 2001.The seven revised full papers went through two rounds of reviewing an improvement. The book addresses key issues in mining Web log data for e-commerce. The papers are devoted to predicting user access, recommender systems and access modeling, and acquiring and modeling data and patterns.