Hybrid Connectionist Approach for Knowledge Discovery from Web Navigation Patterns
type: Dataset
description: In this article, we apply the “EM” algorithm to learning the parameters of a Markov chain mixture model for clus-tering navigation sessions on a Web site. Our main con-tribution is to deduce the model's initial parameters from clusters formed by a hierarchical clustering of a sample of sessions, whose dissimilarity matrix is computed by Dynamic Time Warping. The states of the Markov chains are the neurons of a Kohonen Self Organizing Map, which displays the site as it is seen by the users and also clusters its pages (one neuron corresponding to a cluster of pages). This technique for clustering sessions has been validated on a set of semi-artificial data and the results are excellent. Finally, we tested several criteria for the determination of the optimal number of clusters and con-cluded that the Akaike Information Criterion was best suited to this problem.
issued: 2026-05-10T07:11:50.287770
language: en
modified: 2026-05-10T07:11:50.287775
contact point: Organization
  fn: CCSD
keyword: conference-papers
keyword: connectionist-learning
keyword: infoeu-reposemanticsconferenceobject
keyword: infoinfo-lgcomputer-science-csmachine-learning-cslg
keyword: knowledge-discovery-from-web-navigation-patterns
keyword: web-mining
landing page: IEEE International Conference on Computer Systems and Applications

Hybrid Connectionist Approach for Knowledge Discovery from Web Navigation Patterns
type: Distribution
format: HTML
issued: 2026-05-10T07:11:50.294697
modified: 2026-05-10T07:11:50.279780
access URL: https://hal.science/hal-00084635

test_moissonnage_selune
type: Agent