KMeans¶
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class
pyspark.mllib.clustering.
KMeans
[source]¶ New in version 0.9.0.
Methods
Methods Documentation
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classmethod
train
(rdd, k, maxIterations=100, initializationMode='k-means||', seed=None, initializationSteps=2, epsilon=0.0001, initialModel=None)[source]¶ Train a k-means clustering model.
- Parameters
rdd – Training points as an RDD of Vector or convertible sequence types.
k – Number of clusters to create.
maxIterations – Maximum number of iterations allowed. (default: 100)
initializationMode – The initialization algorithm. This can be either “random” or “k-means||”. (default: “k-means||”)
seed – Random seed value for cluster initialization. Set as None to generate seed based on system time. (default: None)
initializationSteps – Number of steps for the k-means|| initialization mode. This is an advanced setting – the default of 2 is almost always enough. (default: 2)
epsilon – Distance threshold within which a center will be considered to have converged. If all centers move less than this Euclidean distance, iterations are stopped. (default: 1e-4)
initialModel – Initial cluster centers can be provided as a KMeansModel object rather than using the random or k-means|| initializationModel. (default: None)
New in version 0.9.0.
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classmethod