Research map / Learning theory and algorithms / Clustering
An Objective for Hierarchical Clustering in Euclidean Space and Its Connection to Bisecting K-means
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use bisecting k-means rather than average-linkage | Hierarchical clustering · Census, Bank | Hierarchical-Revenue: Census: 4.900e5 -> 4.931e5; Bank: 4.907e5 -> 4.912e5 | Helped |
| Use bisecting k-means rather than single-linkage | Hierarchical clustering · Census, Bank | Hierarchical-Revenue: Census: 4.869e5 -> 4.931e5; Bank: 4.818e5 -> 4.912e5 | Helped |
| Use a data-independent Random split rather than bisecting k-means | Hierarchical clustering · Census, Bank | Hierarchical-Revenue: Census: 4.931e5 -> 1.311e5; Bank: 4.912e5 -> 3.339e5 | Hurt |
| Use average-linkage rather than bisecting k-means | Hierarchical clustering · Census, Bank | CKMM objective: Census: 1.094e12 -> 1.093e12; Bank: 1.049e12 -> 1.052e12 | Mixed |
| Use Random rather than bisecting k-means | Hierarchical clustering · Census, Bank | CKMM objective: Census: 1.094e12 -> 7.463e11; Bank: 1.049e12 -> 7.789e11 | Hurt |
| Use the proposed Hierarchical-Revenue objective rather than CKMM to evaluate Random | Random hierarchical clustering · Census, Bank | Algorithm performance separation | Helped |
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