Name
Title | ||
|---|---|---|
Revealing common sources of image spam by unsupervised clustering with visual features |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1529282.1529474 | SAC |
Keywords | Field | DocType |
similar spam image,color feature,common source,image spam,different setting,text layout,layout feature,background texture,visual feature,unsupervised clustering,data mining technique,cluster computing,clustering,data mining,computer forensics,botnet,difference set | Data mining,Fuzzy clustering,Pattern recognition,Computer forensics,Computer science,Botnet,Image spam,Artificial intelligence,Cluster analysis | Conference |
Citations | PageRank | References |
3 | 0.39 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chengcui Zhang | 1 | 789 | 84.56 |
| Wei-Bang Chen | 2 | 97 | 18.16 |
| Xin Chen | 3 | 98 | 9.56 |
| Gary Warner | 4 | 119 | 12.43 |