The project aims to provide additional protection from unwanted, unsolicited bulk messages to email users by developing a spam filter that will able to learn patterns of spam based on the preferences of the user. The system will be using decision trees to classify whether a particular message is spam by recognizing patterns contained within the email messages which the user deems as spam.

Nowadays, spammers are continually adapting new strategies on how they can fool existing spam classifying softwares. The algorithm we are developing would be able to adapt to new formats spammers use to trick other filters through its ability to learn from the preferences of the user.

"I'm sick and tired of getting spam mails", said student Adrian Lauron. Such reaction is normal for a person who gets annoyed to unsolicited emails as they clutter mailboxes. Irritation is not the only damage spam mails can inflict to users; they can also prolong dial-up connections, wastes bandwidth, and often expose minors to unsuitable contents.

The growing volume of spam mails has challenged programmers to produce anti-spam filtering mostly using Text Categorization techniques to automatically induced UBE filters.