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Conference Papers Year : 2005

Web Usage Mining: Extracting Unexpected Periods from Web Logs

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?”). Those approaches have two main drawbacks. First, they depend on this arbitrary organization of the data. Second, they cannot automatically extract “seasons peaks” among the stored data. In this paper, we propose to perform a specific data mining process (and particularly to extract frequent behaviours) in order to automatically discover the densest periods. Our method extracts, among the whole set of possible combinations, the frequent sequential patterns related to the extracted periods. A period will be considered as dense if it contains at least one frequent sequential pattern for the set of users connected to the Web site 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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Dates and versions

inria-00461877 , version 1 (06-03-2010)

Identifiers

  • HAL Id : inria-00461877 , version 1

Cite

Florent Masseglia, Pascal Poncelet, Maguelonne Teisseire, Alice Marascu. Web Usage Mining: Extracting Unexpected Periods from Web Logs. IEEE 2nd Workshop on Temporal Data Mining (TDM'05). Held in conjunction with ICDM'05, Nov 2005, Houston, United States. ⟨inria-00461877⟩
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