org.apache.spark.mllib.clustering
Class StreamingKMeansModel

Object
  org.apache.spark.mllib.clustering.KMeansModel
      org.apache.spark.mllib.clustering.StreamingKMeansModel

All Implemented Interfaces:

java.io.Serializable, Logging, PMMLExportable, Saveable

public class StreamingKMeansModel
extends KMeansModel
implements Logging

:: Experimental ::

StreamingKMeansModel extends MLlib's KMeansModel for streaming algorithms, so it can keep track of a continuously updated weight associated with each cluster, and also update the model by doing a single iteration of the standard k-means algorithm.

The update algorithm uses the "mini-batch" KMeans rule, generalized to incorporate forgetfullness (i.e. decay). The update rule (for each cluster) is:

 c_t+1 = [(c_t * n_t * a) + (x_t * m_t)] / [n_t + m_t]
 n_t+t = n_t * a + m_t
 

Where c_t is the previously estimated centroid for that cluster, n_t is the number of points assigned to it thus far, x_t is the centroid estimated on the current batch, and m_t is the number of points assigned to that centroid in the current batch.

The decay factor 'a' scales the contribution of the clusters as estimated thus far, by applying a as a discount weighting on the current point when evaluating new incoming data. If a=1, all batches are weighted equally. If a=0, new centroids are determined entirely by recent data. Lower values correspond to more forgetting.

Decay can optionally be specified by a half life and associated time unit. The time unit can either be a batch of data or a single data point. Considering data arrived at time t, the half life h is defined such that at time t + h the discount applied to the data from t is 0.5. The definition remains the same whether the time unit is given as batches or points.

See Also:

Serialized Form

Constructor Summary

StreamingKMeansModel(Vector[] clusterCenters, double[] clusterWeights)

Method Summary

Vector[]	clusterCenters()
double[]	clusterWeights()
StreamingKMeansModel	update(RDD<Vector> data, double decayFactor, String timeUnit) Perform a k-means update on a batch of data.

Methods inherited from class org.apache.spark.mllib.clustering.KMeansModel

computeCost, k, load, predict, predict, predict, save

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.spark.Logging

initializeIfNecessary, initializeLogging, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

Methods inherited from interface org.apache.spark.mllib.pmml.PMMLExportable

toPMML, toPMML, toPMML, toPMML, toPMML

Constructor Detail

StreamingKMeansModel

public StreamingKMeansModel(Vector[] clusterCenters,
                            double[] clusterWeights)

Method Detail

clusterCenters

public Vector[] clusterCenters()

Overrides:

clusterCenters in class KMeansModel

clusterWeights

public double[] clusterWeights()

update

public StreamingKMeansModel update(RDD<Vector> data,
                                   double decayFactor,
                                   String timeUnit)

Perform a k-means update on a batch of data.