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

Class KMeans

java.lang.Object

org.apache.flink.examples.java.clustering.KMeans

public class KMeans
extends Object

This example implements a basic K-Means clustering algorithm.

K-Means is an iterative clustering algorithm and works as follows:
K-Means is given a set of data points to be clustered and an initial set of K cluster centers. In each iteration, the algorithm computes the distance of each data point to each cluster center. Each point is assigned to the cluster center which is closest to it. Subsequently, each cluster center is moved to the center (mean) of all points that have been assigned to it. The moved cluster centers are fed into the next iteration. The algorithm terminates after a fixed number of iterations (as in this implementation) or if cluster centers do not (significantly) move in an iteration.
This is the Wikipedia entry for the K-Means Clustering algorithm.

This implementation works on two-dimensional data points.
It computes an assignment of data points to cluster centers, i.e., each data point is annotated with the id of the final cluster (center) it belongs to.

Input files are plain text files and must be formatted as follows:

• Data points are represented as two double values separated by a blank character. Data points are separated by newline characters.
  For example "1.2 2.3\n5.3 7.2\n" gives two data points (x=1.2, y=2.3) and (x=5.3, y=7.2).
• Cluster centers are represented by an integer id and a point value.
  For example "1 6.2 3.2\n2 2.9 5.7\n" gives two centers (id=1, x=6.2, y=3.2) and (id=2, x=2.9, y=5.7).

Usage: KMeans --points <path> --centroids <path> --output <path> --iterations <n>
If no parameters are provided, the program is run with default data from KMeansData and 10 iterations.

This example shows how to use:

• Bulk iterations
• Broadcast variables in bulk iterations
• Custom Java objects (POJOs)

Note: All Flink DataSet APIs are deprecated since Flink 1.18 and will be removed in a future Flink major version. You can still build your application in DataSet, but you should move to either the DataStream and/or Table API. This class is retained for testing purposes.

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	KMeans.Centroid	A simple two-dimensional centroid, basically a point with an ID.
static class	KMeans.CentroidAccumulator	Sums and counts point coordinates.
static class	KMeans.CentroidAverager	Computes new centroid from coordinate sum and count of points.
static class	KMeans.CountAppender	Appends a count variable to the tuple.
static class	KMeans.Point	A simple two-dimensional point.
static class	KMeans.SelectNearestCenter	Determines the closest cluster center for a data point.

Constructor Summary

Constructors

Constructor	Description
KMeans()

Method Summary

Modifier and Type	Method	Description
static void	main(String[] args)

Methods inherited from class java.lang.Object

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

Constructor Detail

KMeans

public KMeans()

Method Detail

main

public static void main(String[] args)
                 throws Exception

Throws:

Exception