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Association rules for web clickstream analysis

Prof. Erika Blanc and Prof. Paolo Giudici, Data Mining Laboratory University of Pavia/Italy

We present new methodologies for the search of sequence rules in the analysis of web clickstream data.

We start distinguishing direct and indirect sequence rules, and explain the statistical models proposed in each of the two contexts. We then compare statistical and computational models to infer sequential rules as well as the approaches which are prevalent in the main data mining softwares.

We present an application of our ideas to the analysis of a click-stream data set. Our target is to discover which paths are more often followed by the users. The relationships between webpages will be described by sequence rules as well as summary graphical representations.

Keywords: Web Mining, Association rules, Click-stream Data Analysis, Log file.

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