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

Roberta Siciliano (), Antonia D’Ambrosio, Massimo Aria and Sonia Amodio
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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

Journal of Classification, 2016, vol. 33, issue 2, No 8, 298-324

Abstract: 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.

Keywords: Association rules; Sequence rules; Bonferroni inequality; Multidimensional scaling; Non-symmetric correspondence analysis (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s00357-016-9204-8

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