WEBKDD 2002 - Mining Web Data for Discovering Usage Patterns and Profiles | 4th International Workshop, Edmonton, Canada, July 23, 2002, Revised Papers

Osmar R. Zaiane (u. a.)

ISBN 10: 3540203044 / ISBN 13: 9783540203049

Published by Springer Berlin, 2003

About this Item

WEBKDD 2002 - Mining Web Data for Discovering Usage Patterns and Profiles | 4th International Workshop, Edmonton, Canada, July 23, 2002, Revised Papers | Osmar R. Zaiane (u. a.) | Taschenbuch | ix | Englisch | 2003 | Springer Berlin | EAN 9783540203049 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Seller Inventory # 102482593

Bibliographic Details

• Title: WEBKDD 2002 - Mining Web Data for Discovering Usage Patterns and Profiles | 4th International Workshop, Edmonton, Canada, July 23, 2002, Revised Papers
• Publisher: Springer Berlin
• Publication Date: 2003
• Language: English
• ISBN 10: 3540203044
• ISBN 13: 9783540203049
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 11D millimeters 235 millimeters width by 155 millimeters height by 11 millimeters depth
• Weight: 306 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.