Algorithm 1. ClassifyByCluster(scores, iters) |
Inputs: scores - anomaly scores, iters - number of iterations Output: anomaly class centers |
1. center0 = min(scores) 2. center1 = max(scores) 3. labels [scores.length] 4. for n = 1 to iters 5. initialization cnt0,cnt1 6. fori =1 to scores.length 7. diff0 = Math.abs(scores [i]-center0) 8. diff1 = Math.abs(scores [i]-center1) 9. diff0 < diff1? labels [i] = 0&cnt0++:labels [i]=1&cnt1++ 10. end 11. diffs = centers 12. initialization centers 13. update centers 14. if ΔJ<δ 15. break 16. end |
17. returncenter0,center1 |