Clustering similarity digest bloom filters in self-organizing maps
Delaroderie, John C.
MetadataShow full item record
In response to increasing numbers of cases involving digital media, and the increasing sizes of and number of pieces of media in those cases, forensic investigators are relying increasingly on triage techniques for prioritizing which media to review. This thesis describes a framework for clustering documents aquired during a digital forensics investigation on a self organizing(aka Kahonen) map allowing new documents to be categorized relative to existing documents. Furthermore the presented algorithm avoids the need to work with source documents but with sdhash fingerprints allowing a fifty-fold reduction in data required. To test the methodology, document fingerprints are regenerated from the SOM and compared.
Approved for public release; distribution is unlimited.
Showing items related by title, author, creator and subject.
Gupta, Anjum (Monterey, California. Naval Postgraduate School, 2011-03);Automatic text document classification is a fundamental problem in machine learning. Given the dynamic nature and the exponential growth of the World Wide Web, one needs the ability to classify not only a massive number ...
Ha, Anh H.; Costa, Nathaniel P. (Monterey, California: Naval Postgraduate School, 2013-06);The U.S. Army must maximize the efficiency and effectiveness of the documents that facilitate successful materiel requirements generation for the warfighter. Throughout the last decade, incremental modifications to policies ...
Buttrey, Samuel E.; Nolan, Deborah (2001);The spectacular growth and acceptance of theWeb has made it a very attractive medium for interactive documents. Web-based reporting in industry, “live” documents in research, and interactive worksheets in education material ...