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The Oxford Handbook of Computational Linguistics (Oxford Handbooks in Linguistics S.)
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UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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ACSW Frontiers '06 Proceedings of the 2006 Australasian workshops on Grid computing and e-research - Volume 54
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AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
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ACM Transactions on Information Systems (TOIS)
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Computer Networks: The International Journal of Computer and Telecommunications Networking
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The volume of mass unsolicited electronic mail, often known as spam, has recently increased enormously and has become a serious threat to not only the Internet but also to society. This paper proposes a new spam detection method which uses document space density information. Although it requires extensive e-mail traffic to acquire the necessary information, an unsupervised learning engine with a short white list can achieve a 98% recall rate and 100% precision. A direct-mapped cache method contributes handling of over 13,000 e-mails per second. Experimental results, which were conducted using over 50 million actual e-mails of traffic, are also reported in this paper.