Article ID: | iaor20119424 |
Volume: | 51 |
Issue: | 4 |
Start Page Number: | 732 |
End Page Number: | 744 |
Publication Date: | Nov 2011 |
Journal: | Decision Support Systems |
Authors: | Beebe Nicole Lang, Clark Jan Guynes, Dietrich Glenn B, Ko Myung S, Ko Daijin |
Keywords: | graphs, neural networks, statistics: sampling, information theory |
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.