Research map / Learning theory and algorithms / Clustering
Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise Constraints
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the proposed randomized-assignment algorithm instead of ALG-IF | k-means-PBS · Adult, Bank, and Credit; F2, k=10 | constraint violations (%): Adult: 21.81 vs 100.00; Bank: 0.54 vs 99.93; Credit: 14.76 vs 100.00 | Helped |
| Use the proposed randomized-assignment algorithm instead of ALG-IF | k-means-PBS · Adult, Bank, and Credit; F3, k=10 | constraint violations (%): Adult: 0.77 vs 9.42; Bank: 0.50 vs 6.37; Credit: 0.25 vs 3.90 | Helped |
| Use the proposed randomized-assignment algorithm instead of ALG-IF | k-means-PBS · Adult, Bank, and Credit; F2, k=10 | cost of fairness: Adult: 3.48 vs 3.44; Bank: 4.62 vs 4.93; Credit: 2.71 vs 2.66 | No clear effect |
| Use the proposed randomized-assignment algorithm instead of ALG-IF | k-means-PBS · Adult, Bank, and Credit; F3, k=10 | cost of fairness: Adult: 1.24 vs 1.23; Bank: 1.44 vs 1.54; Credit: 1.10 vs 1.11 | No clear effect |
| Use Alg-2 instead of Alg-F(A) | k-center-PBS-CC · Adult, Bank, and Credit; F1, k=10 | constraint violations (%): Adult: .01 vs .00; Bank: .00 vs .00; Credit: .00 vs .00 | Mixed |
| Use Alg-2 instead of Alg-F(B) | k-center-PBS-CC · Adult, Bank, and Credit; F1, k=10 | constraint violations (%): Adult: .01 vs .19; Bank: .00 vs .18; Credit: .00 vs .03 | Helped |
| Use Alg-2 instead of Alg-F(A) | k-center-PBS-CC · Adult, Bank, and Credit; F1, k=10 | maximum cluster radius: Adult: .39 vs .54; Bank: .17 vs .21; Credit: .38 vs .45 | Helped |
| Use Alg-2 instead of Alg-F(B) | k-center-PBS-CC · Adult, Bank, and Credit; F1, k=10 | maximum cluster radius: Adult: .39 vs .31; Bank: .17 vs .12; Credit: .38 vs .28 | Hurt |
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