Traversal Pattern Mining in Web Usage Data

View Sample PDF

Author(s): Jenq-Foung Yao (Georgia College & State University, USA)and Yongqiao Xiao (SAS Co., USA)
Copyright: 2008
Pages: 18
Source title: Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-59904-951-9.ch119

Keywords: Data Mining and Databases / Data Warehousing / Information Science Reference / Library & Information Science

Purchase

View Traversal Pattern Mining in Web Usage Data on the publisher's website for pricing and purchasing information.

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.

The IRMA Community

Research IRM

Traversal Pattern Mining in Web Usage Data

Purchase

Abstract

Related Content

IRMA Sponsors