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Web Usage Mining: Sequential Pattern Extraction with a Very Low Support

Florent Masseglia 1 Doru Tanasa 1 Brigitte Trousse 1 
1 AxIS - Usage-centered design, analysis and improvement of information systems
CRISAM - Inria Sophia Antipolis - Méditerranée , Inria Paris-Rocquencourt
Abstract : The goal of this work is to increase the relevance and the interestingness of patterns discovered by a Web Usage Mining process. Indeed, the sequential patterns extracted on web log files, unless they are found under constraints, often lack interest because of their obvious content. Our goal is to discover minority users' behaviors having a coherence which we want to be aware of (like hacking activities on the Web site or a users' activity limited to a specific part of the Web site). By means of a clustering method on the extracted sequential patterns, we propose a recursive division of the problem. The developed clustering method is based on patterns summaries and neural networks. Our experiments show that we obtain the targeted patterns whereas their extraction by means of a classical process is impossible because of a very weak support (down to 0.006%). The diversity of users' behaviors is so large that the minority ones are both numerous and difficult to locate
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Submitted on : Saturday, February 22, 2014 - 4:50:34 PM
Last modification on : Wednesday, April 6, 2022 - 3:48:26 PM

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Florent Masseglia, Doru Tanasa, Brigitte Trousse. Web Usage Mining: Sequential Pattern Extraction with a Very Low Support. Advanced Web Technologies and Applications: 6th Asia-Pacific Web Conference, APWeb 2004, Apr 2004, Hangzhou, China. pp.513--522, ⟨10.1007/978-3-540-24655-8_56⟩. ⟨hal-00950768⟩



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