public class StreamingKMeansModel extends KMeansModel
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.
|Constructor and Description|
|Modifier and Type||Method and Description|
Perform a k-means update on a batch of data.
computeCost, k, load, predict, predict, predict, save, toPMML, toPMML, toPMML, toPMML
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
public StreamingKMeansModel(Vector clusterCenters, double clusterWeights)
public Vector clusterCenters()
public double clusterWeights()
public StreamingKMeansModel update(RDD<Vector> data, double decayFactor, String timeUnit)