Web Usage Mining: Extracting Unexpected Periods from Web Logs

Florent Masseglia 1 Pascal Poncelet 2 Maguelonne Teisseire 2 Alice Marascu 1
1 AxIS - Usage-centered design, analysis and improvement of information systems
CRISAM - Inria Sophia Antipolis - Méditerranée , Inria Paris-Rocquencourt
2 TATOO - Fouille de données environnementales
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Existing Web usage mining techniques are currently based on an arbitrary division of the data (e.g. “one log per month”) or guided by presumed results (e.g. “what is the customers' behaviour for the period of Christmas purchases?”). These approaches have two main drawbacks. First, they depend on the above-mentioned arbitrary organization of data. Second, they cannot automatically extract “seasonal peaks” from among the stored data. In this paper, we propose a specific data mining process (in particular, to extract frequent behaviour patterns) in order to reveal the densest periods automatically. From the whole set of possible combinations, our method extracts the frequent sequential patterns related to the extracted periods. A period is considered to be dense if it contains at least one frequent sequential pattern for the set of users connected to the website in that period. Our experiments show that the extracted periods are relevant and our approach is able to extract both frequent sequential patterns and the associated dense periods.
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Submitted on : Friday, February 1, 2008 - 11:56:30 AM
Last modification on : Saturday, February 23, 2019 - 7:06:02 PM
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Florent Masseglia, Pascal Poncelet, Maguelonne Teisseire, Alice Marascu. Web Usage Mining: Extracting Unexpected Periods from Web Logs. Data Mining and Knowledge Discovery, Springer, 2008, 16 (1), pp.039-065. ⟨10.1007/s10618-007-0080-z⟩. ⟨lirmm-00204872v2⟩

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