Mining Generalized Web Data for Discovering Usage Patterns

Mining Generalized Web Data for Discovering Usage Patterns

Doru Tanasa (INRIA Sophia Antipolis, France)
Copyright: © 2009 |Pages: 7
DOI: 10.4018/978-1-60566-010-3.ch198
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Abstract

Web Usage Mining (WUM) includes all the Data Mining techniques used to analyze the behavior of a Web site‘s users (Cooley, Mobasher & Srivastava, 1999, Spiliopoulou, Faulstich & Winkler, 1999, Mobasher, Dai, Luo & Nakagawa, 2002). Based mainly on the data stored into the access log files, these methods allow the discovery of frequent behaviors. In particular, the extraction of sequential patterns (Agrawal, & Srikant, 1995) is well suited to the context of Web logs analysis, given the chronological nature of their records. On a Web portal, one could discover for example that “25% of the users navigated on the site in a particular order, by consulting first the homepage then the page with an article about the bird flu, then the Dow Jones index evolution to finally return on the homepage before consulting their personal e-mail as a subscriber”. In theory, this analysis allows us to find frequent behaviors rather easily. However, reality shows that the diversity of the Web pages and behaviors makes this approach delicate. Indeed, it is often necessary to set minimum thresholds of frequency (i.e. minimum support) of about 1% or 2% before revealing these behaviors. Such low supports combined with significant characteristics of access log files (e.g. huge number of records) are generally the cause of failures or limitations for the existent techniques employed in Web usage analysis. A solution for this problem consists in clustering the pages by topic, in the form of a taxonomy for example, in order to obtain a more general behavior. Considering again the previous example, one could have obtained: “70% of the users navigate on the Web site in a particular order, while consulting the home page then a page of news, then a page on financial indexes, then return on the homepage before consulting a service of communication offered by the Web portal”. A page on the financial indexes can relate to the Dow Jones as well as the FTSE 100 or the NIKKEI (and in a similar way: the e-mail or the chat are services of communication, the bird flu belongs to the news section, etc.). Moreover, the fact of grouping these pages under the “financial indexes” term has a direct impact by increasing the support of such behaviors and thus their readability, their relevance and significance. The drawback of using a taxonomy comes from the time and energy necessary to its definition and maintenance. In this chapter, we propose solutions to facilitate (or guide as much as possible) the automatic creation of this taxonomy allowing a WUM process to return more effective and relevant results. These solutions include a prior clustering of the pages depending on the way they are reached by the users. We will show the relevance of our approach in terms of efficiency and effectiveness when extracting the results.
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Background

The structure of a log file is formally described in Definition 7 (at the end of this chapter). This data structure can be easily transformed to the one used by sequential pattern mining algorithms. A record in a log file contains, among other data, the client IP, the date and time of the request, and the Web resource requested. To extract frequent behaviors from such a log file, for each user session in the log file, we first have to: transform the ID-Session into a client number (ID), the date and time into a time number, and the URL into an item number. Table 1 gives a file example obtained after that pre-processing. To each client corresponds a series of times and the URL requested by the client at each time. For instance, the client 2 requested the URL “f” at time d4. The goal is thus, according to definition 2 and by means of a data mining step, to find the sequential patterns in the file that can be considered as frequent. The result may be, for instance, <(a)(c)(b)(c)> (with the file illustrated in table 1 and a minimum support given by the user: 100%). Such a result, once mapped back into URLs, strengthens the discovery of a frequent behavior, common to n users (with n the threshold given for the data mining process) and also gives the sequence of events composing that behavior.

Table 1.
File obtained after a pre-processing step
Client \ Dated1d2d3d4d5
1acdbc
2acbfc
3agcbc

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