Class StreamingKMeansModel
- All Implemented Interfaces:
Serializable
,org.apache.spark.internal.Logging
,PMMLExportable
,Saveable
The update algorithm uses the "mini-batch" KMeans rule, generalized to incorporate forgetfulness (i.e. decay). The update rule (for each cluster) is:
$$ \begin{align} c_{t+1} &= [(c_t * n_t * a) + (x_t * m_t)] / [n_t + m_t] \\ n_{t+1} &= n_t * a + m_t \end{align} $$
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:
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Nested Class Summary
Nested classes/interfaces inherited from class org.apache.spark.mllib.clustering.KMeansModel
KMeansModel.Cluster$, KMeansModel.SaveLoadV1_0$, KMeansModel.SaveLoadV2_0$
Nested classes/interfaces inherited from interface org.apache.spark.internal.Logging
org.apache.spark.internal.Logging.LogStringContext, org.apache.spark.internal.Logging.SparkShellLoggingFilter
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Constructor Summary
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Method Summary
Methods inherited from class org.apache.spark.mllib.clustering.KMeansModel
computeCost, distanceMeasure, k, load, predict, predict, predict, save, trainingCost
Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
Methods inherited from interface org.apache.spark.internal.Logging
initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, isTraceEnabled, log, logDebug, logDebug, logDebug, logDebug, logError, logError, logError, logError, logInfo, logInfo, logInfo, logInfo, logName, LogStringContext, logTrace, logTrace, logTrace, logTrace, logWarning, logWarning, logWarning, logWarning, org$apache$spark$internal$Logging$$log_, org$apache$spark$internal$Logging$$log__$eq, withLogContext
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Constructor Details
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StreamingKMeansModel
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Method Details
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clusterCenters
- Overrides:
clusterCenters
in classKMeansModel
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clusterWeights
public double[] clusterWeights() -
update
Perform a k-means update on a batch of data.- Parameters:
data
- (undocumented)decayFactor
- (undocumented)timeUnit
- (undocumented)- Returns:
- (undocumented)
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