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Analysis of Web Visit Histories, Part I: Distance-Based Visualization of Sequence Rules

Author

Listed:
  • Roberta Siciliano

    (University of Naples Federico II)

  • Antonia D’Ambrosio

    (University of Naples Federico II)

  • Massimo Aria

    (University of Naples Federico II)

  • Sonia Amodio

    (University of Naples Federico II)

Abstract

This paper constitutes Part I of the contribution to the analysis of web visit histories through a new methodological framework. Firstly, web usage and web structure mining are considered as an unique mining process to detect the latent structure of the web navigation across the web sections of a single portal. We extend association rules theory to web data defining new concepts of web (patterns) association and preference matrices, as well as of (indirect and direct) sequence rules. We identify the most significant rules, according to a multiple testing procedure. In the literature, web usage patterns can be visualized in no-distance-based graphs describing the navigation behavior across web pages with sequential arrows. In the following, we introduce a geometrical visualization of sequence rules at any click of the web navigation. In particular, we provide two distance-based visualization methods for the static analysis of all data tout court and the dynamic analysis to discover the most significant web paths click by click. A real world case study is considered throughout the methodological description.

Suggested Citation

  • Roberta Siciliano & Antonia D’Ambrosio & Massimo Aria & Sonia Amodio, 2016. "Analysis of Web Visit Histories, Part I: Distance-Based Visualization of Sequence Rules," Journal of Classification, Springer;The Classification Society, vol. 33(2), pages 298-324, July.
    • Handle: RePEc:spr:jclass:v:33:y:2016:i:2:d:10.1007_s00357-016-9204-8
    DOI: 10.1007/s00357-016-9204-8
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    References listed on IDEAS

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    1. Trevor Cox, 2001. "Multidimensional scaling used in multivariate statistical process control," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(3-4), pages 365-378.
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    Cited by:

    1. Roberta Siciliano & Antonio D’Ambrosio & Massimo Aria & Sonia Amodio, 2017. "Analysis of Web Visit Histories, Part II: Predicting Navigation by Nested STUMP Regression Trees," Journal of Classification, Springer;The Classification Society, vol. 34(3), pages 473-493, October.
    2. Carmela Iorio & Giuseppe Pandolfo & Antonio D’Ambrosio & Roberta Siciliano, 2020. "Mining big data in tourism," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(5), pages 1655-1669, December.

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