Package org.apache.flink.examples.java.clustering

Class Summary

Class	Description
KMeans	This example implements a basic K-Means clustering algorithm.
KMeans.Centroid	A simple two-dimensional centroid, basically a point with an ID.
KMeans.CentroidAccumulator	Sums and counts point coordinates.
KMeans.CentroidAverager	Computes new centroid from coordinate sum and count of points.
KMeans.CountAppender	Appends a count variable to the tuple.
KMeans.Point	A simple two-dimensional point.
KMeans.SelectNearestCenter	Determines the closest cluster center for a data point.