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Traversal Pattern Mining in Web Usage Data
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Author(s): Yongqiao Xiao (Georgia College & State University, USA)and Jenq-Foung (J.F.) Yao (Georgia College & State University, USA)
Copyright: 2004
Pages: 24
Source title:
Web Information Systems
Source Author(s)/Editor(s): David Taniar (Monash University, Australia)and Johanna Wenny Rahayu (La Trobe University, Australia)
DOI: 10.4018/978-1-59140-208-4.ch010
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Abstract
Web usage mining is to discover useful patterns in the web usage data, and the patterns provide useful information about the user’s browsing behavior. This chapter examines different types of web usage traversal patterns and the related techniques used to uncover them, including Association Rules, Sequential Patterns, Frequent Episodes, Maximal Frequent Forward Sequences, and Maximal Frequent Sequences. As a necessary step for pattern discovery, the preprocessing of the web logs is described. Some important issues, such as privacy, sessionization, are raised, and the possible solutions are also discussed.
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