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Artificial Intelligence Review
Erschienen in: Artificial Intelligence Review 3/2020

28.05.2019

A review of alignment based similarity measures for web usage mining

verfasst von: Vinh-Trung Luu, Germain Forestier, Jonathan Weber, Paul Bourgeois, Fahima Djelil, Pierre-Alain Muller

Erschienen in: Artificial Intelligence Review | Ausgabe 3/2020

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Abstract

In order to understand web-based application user behavior, web usage mining applies unsupervised learning techniques to discover hidden patterns from web data that captures user browsing on web sites. For this purpose, web session clustering has been among the most popular approaches to group users with similar browsing patterns that reflect their common interest. An adequate web session clustering implementation significantly depends on the measure that is used to evaluate the similarity of sessions. An efficient approach to evaluate session similarity is sequence alignment, which is known as the task of determining the similarity of elements between sequences. In this paper, we review and compare sequence alignment-based measures for web sessions, and also discuss sequence similarity measures that are not alignment-based. This review also provides a perspective of sequence similarity measures that manipulate web sessions in usage clustering process.
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Metadaten
Titel
A review of alignment based similarity measures for web usage mining
verfasst von
Vinh-Trung Luu
Germain Forestier
Jonathan Weber
Paul Bourgeois
Fahima Djelil
Pierre-Alain Muller
Publikationsdatum
28.05.2019
Verlag
Springer Netherlands
Erschienen in
Artificial Intelligence Review / Ausgabe 3/2020
Print ISSN: 0269-2821
Elektronische ISSN: 1573-7462
DOI
https://doi.org/10.1007/s10462-019-09712-9

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