Security

Comparative Study of Gaussian and Nearest Mean Classifiers for Filtering Spam e-Mails

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Executive Summary

The development of data-mining applications such as classification and clustering has shown the need for machine learning algorithms to be applied to large scale data. The paper gives an overview of some of the most popular machine learning methods (Gaussian and Nearest Mean) and of their applicability to the problem of spam e-mail filtering. The aim of this paper is to compare and investigate the effectiveness of classifiers for filtering spam e-mails using different matrices. Since spam is increasingly becoming difficult to detect, so these automated techniques will help in saving lot of time and resources required to handle e-mail messages.

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