Post-retrieval search hit clustering to improve information retrieval effectiveness: Two digital forensics case studies

  • Authors:

  • Nicole Lang Beebe;Jan Guynes Clark;Glenn B. Dietrich;Myung S. Ko;Daijin Ko

  • Affiliations:

  • Department of Information Systems and Technology Management, The University of Texas at San Antonio, TX, United States;Department of Information Systems and Technology Management, The University of Texas at San Antonio, TX, United States;Department of Information Systems and Technology Management, The University of Texas at San Antonio, TX, United States;Department of Information Systems and Technology Management, The University of Texas at San Antonio, TX, United States;Department of Management Science and Statistics, The University of Texas at San Antonio (UTSA), TX, United States

  • Venue:
  • Decision Support Systems
  • Year:
  • 2011

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Abstract

This research extends text mining and information retrieval research to the digital forensic text string search process. Specifically, we used a self-organizing neural network (a Kohonen Self-Organizing Map) to conceptually cluster search hits retrieved during a real-world digital forensic investigation. We measured information retrieval effectiveness (e.g., precision, recall, and overhead) of the new approach and compared them against the current approach. The empirical results indicate that the clustering process significantly reduces information retrieval overhead of the digital forensic text string search process, which is currently a very burdensome endeavor.