Identifying web navigation behaviour and patterns automatically from clickstream data

  • Authors:

  • I-Hsien Ting;Lillian Clark;Chris Kimble

  • Affiliations:

  • Department of Information Management, National University of Kaohsiung, No. 700 Kaohsiung University Road, 811, Kaohsiung City, Taiwan.;Department of Human Resource and Marketing Management, Portsmouth Business School, University of Portsmouth, Richmond Building, Portland Street, Portsmouth PO1 3DE, UK.;Management Information Systems, Euromed Marseille Ecole de Management, Domaine de Luminy, BP 921, 13288, Marseille Cedex 9, France

  • Venue:
  • International Journal of Web Engineering and Technology
  • Year:
  • 2009

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Abstract

A user's clickstream, such as that which is found in server-side logs, can be a rich source of data concerning the ways in which a user navigates a site, but the volume and level of detail found in these logs makes it difficult to identify and categorise specific navigational patterns. In this paper, we describe the three-step automatic pattern discovery (APD) method, a tool that utilises sequential mining to extract a user's navigation route based on two levels of basic navigational elements. This paper contains descriptions of two studies in which the APD was used; the first makes use of APD to analyse the usage of an educational website; the second describes how APD was used to improve the design of a technical support website in a university department.