Spam, also known as unsolicited bulk email (UBE), is becoming increasingly harmful for email traffics. Filtering is a simple and efficient way to combat against spam. Machine-learning-based classification algorithms are of excellent performance in filtering spam. However, the classifiers need be trained with a group of training samples before being able to work. Heavy manual labor and privacy problems are involved in preparing these training samples. In this paper, we proposed a spam filtering system that can work without training in advance. The system is based on fuzzy clustering instead of classification algorithms, avoiding the problems brought by training samples. The system achieves reasonable filtering quality in the experiments.
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