IDEAS home Printed from https://ideas.repec.org/a/ddj/fseeai/y2011i2p31-36.html
   My bibliography  Save this article

Association and Sequence Mining in Web Usage

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.ann.ugal.ro/eco/Doc2011_2/ClaudiaDinuca.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Vasile MAZILESCU, 2010. "Fuzzy Dynamic Discrimination Algorithms for Distributed Knowledge Management Systems," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 2, pages 15-26.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Claudia Elena DINUCA, 2011. "An Application for Data Preprocessing and Models Extractions in Web Usage Mining," Risk in Contemporary Economy, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, pages 166-176.
    2. Claudia Elena Dinuca & Dumitru Ciobanu, 2011. "On an Algorithm for Identifying Sessions from Web Logs," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 4(4), pages 10-15, August.

    More about this item

    Keywords

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

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ddj:fseeai:y:2011:i:2:p:31-36. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Gianina Mihai (email available below). General contact details of provider: https://edirc.repec.org/data/fegalro.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.