APD-A tool for identifying behavioural patterns automatically from clickstream data

I-Hsien Ting, Lillian Clark, Chris Kimble, Daniel Kudenko, Peter Wright

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Clickstream can be a rich source of data for analysing user behaviour, but the volume of these logs makes it difficult to identify and categorise behavioural patterns. In this paper, we introduce the Automatic Pattern Discovery (APD) method, a technique for automated processing of Clickstream data to identify a user's browsing patterns. The paper also includes case study that is used to illustrate the use of the APD and to evaluate its performance.

Original languageEnglish
Title of host publicationKnowledge-Based Intelligent Information and Engineering Systems: KES 2007 - WIRN 2007, Pt II, Proceedings
EditorsB Apolloni, RJ Howlett, L Jain
Place of PublicationBERLIN
PublisherSpringer
Pages66-73
Number of pages8
ISBN (Print)978-3-540-74826-7
Publication statusPublished - 2007
Event11th International Conference on Knowledge-Based Intelligent Informational and Engineering Systems/17th Italian Workshop on Neural Networks - Vietri sul Mare
Duration: 12 Sept 200714 Sept 2007

Conference

Conference11th International Conference on Knowledge-Based Intelligent Informational and Engineering Systems/17th Italian Workshop on Neural Networks
CityVietri sul Mare
Period12/09/0714/09/07

Keywords

  • web usage mining
  • clickstream data
  • browsing behaviour
  • traversal pattern
  • NAVIGATION

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