• Journal of Korea Multimedia Society
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• v.13 no.6
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• pp.841-848
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• 2010

Conventional filters using email header and body information equally judge whether an incoming email is spam or not. However this is unrealistic in everyday life because each person has different criteria to judge what is spam or not. To resolve this problem, we consider user preference information as well as email category information derived from the email content. In this paper, we have developed a personalized anti-spam system using ontologies constructed from rules derived in a data mining process. The reason why traditional content-based filters are not applicable to the proposed experimental situation is described. In also, several experiments constructing classifiers to decide email category and comparing classification rule learners are performed. Especially, an ID3 decision tree algorithm improved the overall accuracy around 17% compared to a conventional SVM text miner on the decision of email category. Some discussions about the axioms generated from the experimental dataset are given too.

• Journal of the Korea Safety Management & Science
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• v.4 no.2
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• pp.103-111
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• 2002

Nowdays cyber education based on the internet is actively developed. But the management of the customers in the cyber education field is not enough. Then, in this paper, we provide the learner with the proposals of lectures to be extremely matched by analyzing the learning capacity and the greatest concern of him(her) using the methods of data mining, such as RFM, prediction, slickness, association rule, classification, and so on.