Research map / Learning theory and algorithms / Clustering
On Controlling the Size of Clusters in Probabilistic Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use arbitrary-prior binary integer program (Method 1) instead of piecewise-linear mixed integer program (Method 2) (ablation) | size-prior clustering · uniform random data, N=1000 to 10000, negative binomial prior | convergence speed | Helped |
| decrease prior standard deviation from 10 to 0.2 (ablation) | size-prior clustering · artificial 20-cluster data | match of cluster sizes to preferred size 50 | Helped |
| set non-zero probability for empty clusters (p(s=0)=0.9) with K=50 (ablation) | size-prior clustering · artificial 20-cluster data | recovered number of clusters: K=50 -> 22 non-empty, 28 empty | Helped |
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