Cascade simple k-means, selects the best k according to the Calinski-Harabasz criterion. For more information see:
Calinski, T. and J. Harabasz. 1974. A dendrite method for cluster analysis. Commun. Stat. 3: 1-27.
| Usage | Returns |
|---|---|
ee.Clusterer.wekaCascadeKMeans(minClusters, maxClusters, restarts, manual, init, distanceFunction, maxIterations) | Clusterer |
| Argument | Type | Details |
|---|---|---|
minClusters | Integer, default: 2 | Min number of clusters. |
maxClusters | Integer, default: 10 | Max number of clusters. |
restarts | Integer, default: 10 | Number of restarts. |
manual | Boolean, default: false | Manually select the number of clusters. |
init | Boolean, default: false | Set whether to initialize using the probabilistic farthest first like method of the k-means++ algorithm (rather than the standard random selection of initial cluster centers). |
distanceFunction | String, default: "Euclidean" | Distance function to use. Options are: Euclidean & Manhattan |
maxIterations | Integer, default: null | Maximum number of iterations for k-means. |
Examples
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