APD-A Tool for Identifying Behavioural Patterns Automatically from Clickstream Data

Authors:
I-Hsien Ting;Lillian Clark;Chris Kimble;Daniel Kudenko;Peter Wright
Affiliations:
Department of Computer Science, The University of York, Heslington, York YO105DD, United Kingdom;Department of Computer Science, The University of York, Heslington, York YO105DD, United Kingdom;Department of Computer Science, The University of York, Heslington, York YO105DD, United Kingdom;Department of Computer Science, The University of York, Heslington, York YO105DD, United Kingdom;Department of Computer Science, The University of York, Heslington, York YO105DD, United Kingdom
Venue:
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
Year:
2007

Citing 6
Cited 0

Improving Web Usability Through Visualization

IEEE Internet Computing
Visual Analysis of Website Browsing Patterns

Visual Interfaces to Digital Libraries [JCDL 2002 Workshop]
Discovery of Interesting Usage Patterns from Web Data

WEBKDD '99 Revised Papers from the International Workshop on Web Usage Analysis and User Profiling
Mining Web Log Sequential Patterns with Position Coded Pre-Order Linked WAP-Tree

Data Mining and Knowledge Discovery
An efficient data mining algorithm for discovering web access patterns

APWeb'03 Proceedings of the 5th Asia-Pacific web conference on Web technologies and applications
A pattern restore method for restoring missing patterns in server side clickstream data

APWeb'05 Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development

Quantified Score

Hi-index	0.00

Visualization

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.