Data mining based intelligent analysis of threatening e-mail
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.
data mining classification threatening e-mail detection