Using Glocal Event Alignment for Comparing Sequences of Significantly Different Lengths

  • Vinh-Trung Luu
  • Mathis Ripken
  • Germain Forestier
  • Frédéric Fondement
  • Pierre-Alain Muller
Conference paper

DOI: 10.1007/978-3-319-41920-6_5

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9729)
Cite this paper as:
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. Lecture Notes in Computer Science, vol 9729. Springer, Cham

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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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Vinh-Trung Luu
    • 1
  • Mathis Ripken
    • 1
  • Germain Forestier
    • 1
  • Frédéric Fondement
    • 1
  • Pierre-Alain Muller
    • 1
  1. 1.MIPSUniversité de Haute AlsaceMulhouse CedexFrance

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