Semantic analysis of user behaviors for detecting spam mail

According to continuous increasing of spam email, 92.6% of recent total email is known spam email. In this research, we will show an adaptive learning system that filter spam emails based on user's action pattern as time goes by. In this paper, we consider relationship between user's actions such as what action is took after one action and how long does it take. They analyze that each action has how much meaning, and that it has an effect on filtering spam emails. And that in turn determines weight for each email. In experimentation, we will compare results of system of this research and weighted Bayesian classifier using real email dataset. Also, we will show how to handle personalization for concept drift and adaptive learning.