Paper Title
Enhanced Spam Filtering With Fuzzy Similarity

Spam is an unsolicited bulk mail or junk email. Spam emails not only waste resources such as bandwidth, storage and computation power, but also the time and energy of email receivers who must search for legitimate emails among the spam and take action to dispose the spam. So for overcoming these problems Spam filtering techniques are developed which classify messages among two categories, spam and non-spam. Different decision tree algorithms are studied for spam classification then they are compared analyzed and evaluated on the basis of various measures as Feature preprocessing, Feature Extraction, Measure of best split, Types of Attributes, and Detection rate . Finally fuzzy similarity measure algorithm is proposed which gives higher accuracy and low false positive and low false negative rate.