smile.clustering

Class DENCLUE

java.lang.Object

smile.clustering.PartitionClustering<double[]>

smile.clustering.DENCLUE

All Implemented Interfaces:

java.io.Serializable, Clustering<double[]>

public class DENCLUE
extends PartitionClustering<double[]>

DENsity CLUstering. The DENCLUE algorithm employs a cluster model based on kernel density estimation. A cluster is defined by a local maximum of the estimated density function. Data points going to the same local maximum are put into the same cluster.

Clearly, DENCLUE doesn't work on data with uniform distribution. In high dimensional space, the data always look like uniformly distributed because of the curse of dimensionality. Therefore, DENCLUDE doesn't work well on high-dimensional data in general.

References

1. A. Hinneburg and D. A. Keim. A general approach to clustering in large databases with noise. Knowledge and Information Systems, 5(4):387-415, 2003.
2. Alexander Hinneburg and Hans-Henning Gabriel. DENCLUE 2.0: Fast Clustering based on Kernel Density Estimation. IDA, 2007.

See Also:

Serialized Form

Field Summary

Fields inherited from class smile.clustering.PartitionClustering

k, size, y

Fields inherited from interface smile.clustering.Clustering

OUTLIER

Constructor Summary

Constructors

Constructor and Description
DENCLUE(double[][] data, double sigma, int m) Constructor.

Method Summary

Modifier and Type	Method and Description
double[][]	getDensityAttractors() Returns the density attractors of cluster.
double	getSigma() Returns the smooth (standard deviation) parameter in the Gaussian kernel.
int	predict(double[] x) Cluster a new instance.
java.lang.String	toString()

Methods inherited from class smile.clustering.PartitionClustering

getClusterLabel, getClusterSize, getNumClusters, seed, seed

Methods inherited from class java.lang.Object

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

Constructor Detail

DENCLUE

public DENCLUE(double[][] data,
               double sigma,
               int m)

Constructor. Clustering data.

Parameters:

data - the dataset for clustering.

sigma - the smooth parameter in the Gaussian kernel. The user can choose sigma such that number of density attractors is constant for a long interval of sigma.

m - the number of selected samples used in the iteration. This number should be much smaller than the number of data points to speed up the algorithm. It should also be large enough to capture the sufficient information of underlying distribution.

Method Detail

getSigma

public double getSigma()

Returns the smooth (standard deviation) parameter in the Gaussian kernel.

Returns:

the smooth (standard deviation) parameter in the Gaussian kernel.

getDensityAttractors

public double[][] getDensityAttractors()

Returns the density attractors of cluster.

predict

public int predict(double[] x)

Description copied from interface: Clustering

Cluster a new instance.

Parameters:

x - a new instance.

Returns:

the cluster label.

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object