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International Conference on Machine Learning and Data Mining in Pattern Recognition

MLDM 2016: Machine Learning and Data Mining in Pattern Recognition pp 58–72Cite as

Using Glocal Event Alignment for Comparing Sequences of Significantly Different Lengths

Part of the Lecture Notes in Computer Science book series (LNAI,volume 9729)

Abstract

This work takes place in the context of conversion rate optimization by enhancing the user experience during navigation on e-commerce web sites. The requirement is to be able to segment visitors into meaningful clusters, which can then be targeted with specific call-to-actions, in order to increase the web site turnover. This paper presents an original approach, which equally combines global- and local-alignment techniques (Needleman-Wunsch and Smith-Waterman) in order to automatically segment visitors according to the sequence of visited pages. Experimental results on synthetic datasets show that our approach out-performs other typically used alignment metrics, such as hybrid approaches or Dynamic Time Warping.

Keywords

  • Web mining
  • Sequential pattern mining
  • Clustering

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  • DOI: 10.1007/978-3-319-41920-6_5
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Authors and Affiliations

  1. MIPS, Université de Haute Alsace, 12, rue des frères Lumière, 68093, Mulhouse Cedex, France

    Vinh-Trung Luu, Mathis Ripken, Germain Forestier, Frédéric Fondement & Pierre-Alain Muller

Authors
  1. Vinh-Trung Luu
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  2. Mathis Ripken
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  3. Germain Forestier
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  4. Frédéric Fondement
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  5. Pierre-Alain Muller
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Corresponding author

Correspondence to Vinh-Trung Luu .

Editor information

Editors and Affiliations

  1. IBaI, Inst of Comp Vision and applied Comp Sci, Leipzig, Sachsen, Germany

    Petra Perner

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Luu, VT., Ripken, M., Forestier, G., Fondement, F., Muller, PA. (2016). Using Glocal Event Alignment for Comparing Sequences of Significantly Different Lengths. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2016. Lecture Notes in Computer Science(), vol 9729. Springer, Cham. https://doi.org/10.1007/978-3-319-41920-6_5

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  • DOI: https://doi.org/10.1007/978-3-319-41920-6_5

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