Two Approaches on Implementation of CBR and CRM Technologies to the Spam Filtering Problem

Recently the number of undesirable messages coming to e-mail has strongly increased. As spam has changeable char-acter the anti-spam systems should be trainable and dynamical. The machine learning technology is successfully applied in a filtration of e-mail from undesirable messages for a long time. In this paper it is offered to apply Case Based Rea-soning technology to a spam filtering problem. The possibility of continuous updating of spam templates base on the bases of which new coming spam messages are compared, will raise efficiency of a filtration. Changing a combination of conditions it is possible to construct flexible filtration system adapted for different users or corporations. Also in this paper it is considered the second approach as implementation of CRM technology to spam filtration which is not ap-plied to this area yet