Data mining based intelligent analysis of threatening e-mail |
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| Authors: | S Appavu R Rajaram M Muthupandian G Athiappan KS Kashmeera |
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| Affiliation: | 1. Institute of New Energy and Low-Carbon Technology, Sichuan University, Chengdu 610064, PR China;2. Uncertainty Decision-Making Laboratory, Sichuan University, Chengdu 610064, PR China;1. Telfer School of Management, University of Ottawa, 55 Laurier Ave. East, Ottawa, ON, K1N 6N5, Canada;2. SUNLAB Centre for Research in Photonics, University of Ottawa, 800 King Edward, Ottawa, K1N 6N5, Canada;1. VUI Center for Outcomes Research Analytics and Evaluation, Henry Ford Health System, Detroit, MI, USA;2. Center for Surgery and Public Health, Brigham and Women''s Hospital, Harvard Medical School, Boston, MA, USA;3. Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women''s Hospital, Harvard Medical School, Boston, MA, USA;4. Department of Urology, Mayo Clinic, Rochester, MN, USA;5. Department of Urology, University of Hamburg Eppendorf, Hamburg, Germany;6. Department of Urology, Urological Research Institute, University Vita-Salute San Raffaele, Milan, Italy;7. Memorial Sloan Kettering Cancer Center, New York, NY, USA;8. Division of Urologic Surgery, Brigham and Women''s Hospital/Dana-Farber Cancer Institute, Boston, MA, USA |
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| Abstract: | This paper proposed a decision tree based classification method to detect e-mails that contain terrorism information. The proposed classification method is an incremental and user-feedback based extension of a decision tree induction algorithm named Ad Infinitum. We show that Ad Infinitum algorithm is a good choice for threatening e-mail detection as it runs fast on large and high dimensional databases, is easy to tune and is highly accurate, outperforming popular algorithms such as Decision Trees, Support Vector Machines and Naive Bayes. In particular, we are interested in detecting fraudulent and possibly criminal activities from such e-mails. |
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