Research map / Learning theory and algorithms / Clustering
GoT: a Growing Tree Model for Clustering Ensemble
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use GoT instead of DREC | clustering ensemble · Iris | ARI: 0.861±0.000 -> 0.871±0.000 | Helped |
| Use GoT instead of WTQ | clustering ensemble · Wine recognition data | ARI: 0.754±0.022 -> 0.821±0.002 | Helped |
| Use GoT instead of EAC | clustering ensemble · Glass Identification Database | ARI: 0.261±0.000 -> 0.272±0.000 | Helped |
| Use GoT instead of EAC | clustering ensemble · Ecoli | ARI: 0.540±0.008 -> 0.558±0.006 | Helped |
| Use GoT instead of EAC | clustering ensemble · LIBRAS Movement Database | ARI: 0.332±0.001 -> 0.342±0.000 | Helped |
| Use GoT instead of USENC | clustering ensemble · Image Segmentation data | ARI: 0.511±0.000 -> 0.516±0.000 | Helped |
| Refine the co-association matrix with shortest paths before prototype discovery | GoT · synthetic data | prototype cluster representation | Helped |
| Grow trees from prototypes by repeatedly assigning high-margin samples | GoT · eight UCI data sets and eight image data sets | ARI | Helped |
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