ee.Clusterer.wekaXMeans

X-Means is K-Means with an efficient estimation of the number of clusters. For more information see:

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.

UsageReturns
ee.Clusterer.wekaXMeans(minClusters, maxClusters, maxIterations, maxKMeans, maxForChildren, useKD, cutoffFactor, distanceFunction, seed)Clusterer
ArgumentTypeDetails
minClustersInteger, default: 2

Minimum number of clusters.

maxClustersInteger, default: 8

Maximum number of clusters.

maxIterationsInteger, default: 3

Maximum number of overall iterations.

maxKMeansInteger, default: 1000

The maximum number of iterations to perform in KMeans.

maxForChildrenInteger, default: 1000

The maximum number of iterations in KMeans that is performed on the child centers.

useKDBoolean, default: false

Use a KDTree.

cutoffFactorFloat, default: 0

Takes the given percentage of the splitted centroids if none of the children win.

distanceFunctionString, default: "Euclidean"

Distance function to use. Options are: Chebyshev, Euclidean & Manhattan.

seedInteger, default: 10

The randomization seed.

Examples

JavaScript

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Python

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