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

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

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Article ID: iaor20119424
Volume: 51
Issue: 4
Start Page Number: 732
End Page Number: 744
Publication Date: Nov 2011
Journal: Decision Support Systems
Authors: , , , ,
Keywords: graphs, neural networks, statistics: sampling, information theory
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.

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