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An Application for Data Preprocessing and Models Extractions in Web Usage Mining

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  • Claudia Elena DINUCA

    (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. The goal of this application is to analyze 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. In this paper we will focus on displaying the way how it was implemented the application for data preprocessing and extracting different data models from web logs data, finding association as a data mining technique to extract potentially useful knowledge from web usage data. We find different data models navigation patterns by analysing the log files of the web-site. I implemented the application in Java using NetBeans IDE. For exemplification, I used the log files data from a commercial web site www.nice-layouts.com.

Suggested Citation

  • 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.
    • Handle: RePEc:ddj:fserec:y:2011:p:166-176
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    References listed on IDEAS

    as
    1. Dinuca Claudia Elena, 2011. "The Process of Data Preprocessing for Web Usage Data Mining through a Complete Example," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 610-612, May.
    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.
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    1. 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.
    2. 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.

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