Affinity analysis is one kind of data mining investigation. In this approach, the goal is to see what association rules if any exist, i.e., what actions co-occur. In the context of Web usage, an affinity analysis might yield a rule such as ‘if page A is visited, then page D is visited’ which might indicate a previously unknown navigational path popular among users. Affinity analysis is also sometime referred to as market basket analysis, as it can provide retailers with information about products that consumers purchase together.
Published in Chapter:
Mining User Activity Data In Higher Education Open Systems: Trends, Challenges, and Possibilities
Owen G. McGrath (University of California at Berkeley, USA)
Copyright: © 2009
|Pages: 14
DOI: 10.4018/978-1-60566-400-2.ch032
Abstract
Higher education IT project managers have always relied on user activity data as logged in one form or another. Summarized counts of users and performance trends serve as essential sources of information for those who need to analyze problems, monitor security, improve software, perform capacity planning, etc. With the reach of the Internet extending into all aspects of higher education research and teaching, however, new questions have arisen as to how, where, and when user activity gets captured and analyzed. Tracking and understanding remote users and their round-the-clock activities is a major technical and analytical challenge within today’s cyber-infrastructure. As open content publishing and open source development projects thrive in higher education there are some side effects on usage analysis. This chapter examines how data mining solutions – particularly Web usage mining methods– are being taken up in three open systems project management contexts: digital libraries, online museums, and course management systems. In describing the issues and challenges that motivate data mining applications in these three contexts, the chapter provides an overview of how data mining integrates within project management processes. The chapter also touches on ways in which data mining can be augmented by the complementary practice of data visualization.