Mining web navigations for intelligence

  • Authors:
  • Harris Wu;Michael Gordon;Kurtis DeMaagd;Weiguo Fan

  • Affiliations:
  • Old Dominion University, Norfolk, VA 23529, United States;University of Michigan Business School, 701 Tappan Street, Ann Arbor, MI 48103, United States;University of Michigan Business School, 701 Tappan Street, Ann Arbor, MI 48103, United States;Virginia Tech, Blacksburg, VA 24061, United States

  • Venue:
  • Decision Support Systems - Special issue: Intelligence and security informatics
  • Year:
  • 2006

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Abstract

The Internet is one of the fastest growing areas of intelligence gathering. We present a statistical approach, called principal clusters analysis, for analyzing millions of user navigations on the Web. This technique identifies prominent navigation clusters on different topics. Furthermore, it can determine information items that are useful starting points to explore a topic, as well as key documents to explore the topic in greater detail. Trends can be detected by observing navigation prominence over time. We apply this technique on a large popular website. The results show promise in web intelligence mining.