In recent years the highestdegree of communication happens through e-mails which are often affected by passive or active attacks. Effective spam filtering measures are the timely requirement to handle such attacks. Many efficient spam filters are available now-a-days with different degrees of performance and usually the accuracy level varies between 60-80% on an average. But most of the filtering techniques are unable to handle frequent changing scenario of spam mails adopted by the spammers over the time. Therefore improved spam control algorithms or enhancing the efficiency of various existing data mining algorithms to its fullest extent are the utmost requirement. In this paper three types of decision tree classifying techniques which are basically data mining classifiers namely Naïve Bayes Tree classifier (NBT), C 4. 5 (or J48) decision tree classifier and Logistic Model Tree classifier (LMT) are studied and analyzed for spam mail filtration. The test results depict that LMT is giving the most efficient result in terms of performance with almost 90% accuracy level to detect spam mails and non-spam (HAM) mails.

Spam Ham data Mining naïve Bayes Tree Logistic Model Tree Machinelearning Spam-score