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A review of alignment based similarity measures for web usage mining

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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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Acknowledgements

The authors would like to thanks the Beampulse company for providing datasets to perform experiments. They also like to thanks VIET and Campus France for funding this research.

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  1. IRIMAS, Université de Haute-Alsace, 12 Rue des Frères Lumière, 68093, Mulhouse, France

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

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  1. Vinh-Trung Luu
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  2. Germain Forestier
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  3. Jonathan Weber
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  4. Paul Bourgeois
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  5. Fahima Djelil
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Correspondence to Germain Forestier.

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Luu, VT., Forestier, G., Weber, J. et al. A review of alignment based similarity measures for web usage mining. Artif Intell Rev 53, 1529–1551 (2020). https://doi.org/10.1007/s10462-019-09712-9

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