Abstract

The increase in unwanted spam email volumes created a clear need for more effective and robust anti-spam filters. Recent machine learning methods are employed to detect and process spam mails successfully. In this paper, we present a density based clustering for email classification problem using kNN algorithm. Initially, the relevant features for filtering the spam messages are extracted from the study and it acts as an antispam filter. It thereby generates the successful corpus list for detection of spam emails. The experiments are conducted on various email datasets and the results show that the proposed kNN density based clustering offers improved performance than the other methods. As shown by the test results, our methodology showed stronger prediction capabilities and better classifications based on in-depth learning techniques.