Title: Discovering communities for web usage mining systems

Authors: Yacine Slimani; Abdelouaheb Moussaoui; Yves Lechevallier; Ahlem Drif

Addresses: Laboratory of Intelligent Systems, Department of Computer Science, University Ferhat Abbas Setif 1, Setif 19000, Algeria ' Laboratory of Intelligent Systems, Department of Computer Science, University Ferhat Abbas Setif 1, Setif 19000, Algeria ' Laboratory of Intelligent Systems, Department of Computer Science, University Ferhat Abbas Setif 1, Setif 19000, Algeria ' Laboratory of Intelligent Systems, Department of Computer Science, University Ferhat Abbas Setif 1, Setif 19000, Algeria

Abstract: Discovering the community structure in the context of web usage mining has been addressed in many different ways. In this paper, we present a new method for detecting communities using Markov chains based on the set of frequent motifs. The basic idea is to analyse the occurrence probability of different frequent sequences during different user sessions in order to extract the communities that describe the users' behaviour. The proposed method is successfully applied on the website of Setif University.

Keywords: web usage mining; community detection; complex networks; Markov chains; quality function.

DOI: 10.1504/IJAIP.2019.098575

International Journal of Advanced Intelligence Paradigms, 2019 Vol.12 No.3/4, pp.331 - 354

Received: 03 Feb 2016
Accepted: 27 Oct 2016
Published online: 28 Mar 2019 *

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