Revealing common sources of image spam by unsupervised clustering with visual features
Chengcui Zhang, Wei-Bang Chen, Xin Chen, Gary Warner · 2009
In this paper, we investigate image spam with data mining techniques in order to reveal the common sources of unsolicited emails. To identify the origins, a two-stage clustering method groups visually similar spam images by exploring their visual features, including color feature, layout feature, text layout, and background textures. We test the proposed approach under different settings and combinations of features and measure the performance with a modified F-measure.