This research manages in-depth analysis on the knowledge about spams and expects to
propose an efficient spam filtering method with the ability of adapting to the dynamic environment.
We focus on the analysis of email’s header and apply decision tree data mining
technique to look for the association rules about spams. Then, we propose an efficient systematic
filtering method based on these association rules. Our systematic method has the
following major advantages: (1) Checking only the header sections of emails, which is different
from those spam filtering methods at present that have to analyze fully the email’s content.
Meanwhile, the email filtering accuracy is expected to be enhanced. (2) Regarding the
solution to the problem of concept drift, we propose a window-based technique to estimate
for the condition of concept drift for each unknown email, which will help our filtering method
in recognizing the occurrence of spam. (3) We propose an incremental learning mechanism
for our filtering method to strengthen the ability of adapting to the dynamic environment.
