Generating Dynamic Higher-Order Markov Models in Web Usage Mining

• Type: Article in International Conference Proceedings Book
• Date: 2005

Authors

José Borges (Author) FEUP; Mark Levene (Author) Other

Conference proceedings International

Title: Knowledge Discovery in Databases: PKDD 2005

Pages: 34-45

9th European Conference on Principles and Practice of Knowledge Discovery in Databases - PKDD 2005

Porto, Portugal, October 3-7, 2005

Indexing

ISI Proceedings

ISI Web of Knowledge - 12 Citations

Scopus - 26 Citations

Scientific classification

FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering

Other information

Authenticus ID: P-000-65T

DOI: 10.1007/11564126_9

Abstract (EN): Markov models have been widely used for modelling users’ web navigation behaviour. In previous work we have presented a dynamic clustering-based Markov model that accurately represents second-order transition probabilities given by a collection of navigation sessions. Herein, we propose a generalisation of the method that takes into account higher-order conditional probabilities. The method makes use of the state cloning concept together with a clustering technique to separate the navigation paths that reveal differences in the conditional probabilities. We report on experiments conducted with three real world data sets. The results show that some pages require a long history to understand the users choice of link, while others require only a short history. We also show that the number of additional states induced by the method can be controlled through a probability threshold parameter.

Language: English

Type (Professor's evaluation): Scientific