Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Journal of the American Society for Information Science and Technology
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Visualizing authorship for identification
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
A novel approach of mining write-prints for authorship attribution in e-mail forensics
Digital Investigation: The International Journal of Digital Forensics & Incident Response
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It is easy to hide the true identity of the author of an email. The author's actual name, email address, etc. can be changed arbitrarily to deceive an email receiver. For example, a sender can change his/her identity in the email header to send different emails to various recipients. Therefore, in this paper, we investigate techniques for authorship similarity detection from the text content of a short length, topic-free email. 150 stylistic cues are identified for this problem. A frequent pattern and machine learning based method is proposed. Extensive experiment results are also presented for the Enron email data set.