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Association and Sequence Mining in Web Usage

Author

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  • Claudia Elena DINUCA

    () (Faculty of Economics and Business Administration, University of Craiova. Romania)

Abstract

Web servers worldwide generate a vast amount of information on web users’ browsing activities. Several researchers have studied these so-called clickstream or web access log data to better understand and characterize web users. Clickstream data can be enriched with information about the content of visited pages and the origin (e.g., geographic, organizational) of the requests. The goal of this project is to analyse user behaviour by mining enriched web access log data. With the continued growth and proliferation of e-commerce, Web services, and Web-based information systems, the volumes of click stream and user data collected by Web-based organizations in their daily operations has reached astronomical proportions. This information can be exploited in various ways, such as enhancing the effectiveness of websites or developing directed web marketing campaigns. The discovered patterns are usually represented as collections of pages, objects, or re-sources that are frequently accessed by groups of users with common needs or interests. The focus of this paper is to provide an overview how to use frequent pattern techniques for discovering different types of patterns in a Web log database. In this paper we will focus on finding association as a data mining technique to extract potentially useful knowledge from web usage data. I implemented in Java, using NetBeans IDE, a program for identification of pages’ association from sessions. For exemplification, we used the log files from a commercial web site.

Suggested Citation

  • Claudia Elena DINUCA, 2011. "Association and Sequence Mining in Web Usage," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 2, pages 31-36.
  • Handle: RePEc:ddj:fseeai:y:2011:i:2:p:31-36
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    File URL: http://www.ann.ugal.ro/eco/Doc2011_2/ClaudiaDinuca.pdf
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    References listed on IDEAS

    as
    1. Lillian Clark & I-Hsien Ting & Chris Kimble & P. C. Wright & Daniel Kudenko, 2006. "Combining Ethnographic and Clickstream Data to Identify User Web Browsing Strategies," Post-Print halshs-00489627, HAL.
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    More about this item

    Keywords

    Clickstream analysis; Web server logs; Association rules; Sessions identification; Apriori algorithm; Web usage mining; Sequence rule mining;

    JEL classification:

    • A12 - General Economics and Teaching - - General Economics - - - Relation of Economics to Other Disciplines
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • L21 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Business Objectives of the Firm
    • M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management
    • M21 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics - - - Business Economics

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