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ee.Clusterer.wekaXMeans

  • X-Means extends K-Means by efficiently estimating the number of clusters.

  • Theee.Clusterer.wekaXMeans function is used with various parameters to configure the clustering process.

  • Parameters such asminClusters,maxClusters,maxIterations, and distance function can be specified.

 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: 2Minimum number of clusters.
maxClustersInteger, default: 8Maximum number of clusters.
maxIterationsInteger, default: 3Maximum number of overall iterations.
maxKMeansInteger, default: 1000The maximum number of iterations to perform in KMeans.
maxForChildrenInteger, default: 1000The maximum number of iterations in KMeans that is performed on the child centers.
useKDBoolean, default: falseUse a KDTree.
cutoffFactorFloat, default: 0Takes the given percentage of the split centroids if none of the children win.
distanceFunctionString, default: "Euclidean"Distance function to use. Options are: Chebyshev, Euclidean, and Manhattan.
seedInteger, default: 10The randomization seed.

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Last updated 2024-07-13 UTC.