AI-generated Key Takeaways
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X-Means extends K-Means by efficiently estimating the number of clusters.
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The
ee.Clusterer.wekaXMeansfunction is used with various parameters to configure the clustering process. -
Parameters such as
minClusters,maxClusters,maxIterations, and distance function can be specified.
Dan Pelleg, Andrew W. Moore: X-means: Extending K-means with Efficient Estimation of the Number of Clusters. In: Seventeenth International Conference on Machine Learning, 727-734, 2000.
| Usage | Returns |
|---|---|
ee.Clusterer.wekaXMeans( minClusters
, maxClusters
, maxIterations
, maxKMeans
, maxForChildren
, useKD
, cutoffFactor
, distanceFunction
, seed
)
|
Clusterer |
| Argument | Type | Details |
|---|---|---|
minClusters
|
Integer, default: 2 | Minimum number of clusters. |
maxClusters
|
Integer, default: 8 | Maximum number of clusters. |
maxIterations
|
Integer, default: 3 | Maximum number of overall iterations. |
maxKMeans
|
Integer, default: 1000 | The maximum number of iterations to perform in KMeans. |
maxForChildren
|
Integer, default: 1000 | The maximum number of iterations in KMeans that is performed on the child centers. |
useKD
|
Boolean, default: false | Use a KDTree. |
cutoffFactor
|
Float, default: 0 | Takes the given percentage of the split centroids if none of the children win. |
distanceFunction
|
String, default: "Euclidean" | Distance function to use. Options are: Chebyshev, Euclidean, and Manhattan. |
seed
|
Integer, default: 10 | The randomization seed. |
