Package smile.clustering

Class KModes

java.lang.Object

smile.clustering.PartitionClustering

smile.clustering.CentroidClustering<int[],int[]>

smile.clustering.KModes

All Implemented Interfaces:

Serializable, Comparable<CentroidClustering<int[],int[]>>

public class KModes
extends CentroidClustering<int[],int[]>

K-Modes clustering. K-Modes is the binary equivalent for K-Means. The mean update for centroids is replace by the mode one which is a majority vote among element of each cluster.

References

1. Joshua Zhexue Huang. Clustering Categorical Data with k-Modes.

See Also:

• KMeans
• Serialized Form

Field Summary

Fields inherited from class smile.clustering.CentroidClustering

centroids, distortion

Fields inherited from class smile.clustering.PartitionClustering

k, OUTLIER, size, y

Constructor Summary

Constructors

Constructor	Description
KModes(double distortion, int[][] centroids, int[] y)	Constructor.

Method Summary

Modifier and Type	Method	Description
protected double	distance(int[] x, int[] y)	The distance function.
static KModes	fit(int[][] data, int k)	Fits k-modes clustering.
static KModes	fit(int[][] data, int k, int maxIter)	Fits k-modes clustering.

Methods inherited from class smile.clustering.CentroidClustering

compareTo, predict, toString

Methods inherited from class smile.clustering.PartitionClustering

run, seed

Methods inherited from class java.lang.Object

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

Constructor Details

KModes

public KModes(double distortion,
 int[][] centroids,
 int[] y)

Constructor.

Parameters:

distortion - the total distortion.

centroids - the centroids of each cluster.

y - the cluster labels.

Method Details

distance

protected double distance(int[] x,
 int[] y)

Description copied from class: CentroidClustering

The distance function.

Specified by:

distance in class CentroidClustering<int[],int[]>

Parameters:

x - an observation.

y - the other observation.

Returns:

the distance.

fit

public static KModes fit(int[][] data,
 int k)

Fits k-modes clustering.

Parameters:

data - the input data of which each row is an observation.

k - the number of clusters.

Returns:

the model.

fit

public static KModes fit(int[][] data,
 int k,
 int maxIter)

Fits k-modes clustering.

Parameters:

data - the input data of which each row is an observation.

k - the number of clusters.

maxIter - the maximum number of iterations.

Returns:

the model.