Package elki.clustering.kmeans

Class KMeansMinusMinus<V extends NumberVector>

java.lang.Object

elki.clustering.kmeans.AbstractKMeans<V,KMeansModel>

elki.clustering.kmeans.KMeansMinusMinus<V>

Type Parameters:

V - vector datatype

All Implemented Interfaces:

Algorithm, ClusteringAlgorithm<Clustering<KMeansModel>>, KMeans<V,KMeansModel>

@Title("K-Means--")
@Reference(authors="S. Chawla, A. Gionis",
           title="k-means--: A Unified Approach to Clustering and Outlier Detection",
           booktitle="Proc. 13th SIAM Int. Conf. on Data Mining (SDM 2013)",
           url="https://doi.org/10.1137/1.9781611972832.21",
           bibkey="DBLP:conf/sdm/ChawlaG13")
public class KMeansMinusMinus<V extends NumberVector>
extends AbstractKMeans<V,KMeansModel>

k-means--: A Unified Approach to Clustering and Outlier Detection.

Similar to Lloyds K-means algorithm, but ignores the farthest points when updating the means, considering them to be outliers.

Reference:

S. Chawla, A. Gionis
k-means--: A Unified Approach to Clustering and Outlier Detection
Proc. 13th SIAM Int. Conf. on Data Mining (SDM 2013)

Since:

0.7.5

Author:

Jonas Steinke

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
protected class	KMeansMinusMinus.Instance	Inner instance, storing state for a single data set.
static class	KMeansMinusMinus.Par<V extends NumberVector>	Parameterization class.

Nested classes/interfaces inherited from interface elki.Algorithm

Algorithm.Utils

Field Summary

Fields

Modifier and Type	Field	Description
private static Logging	LOG	The logger for this class.
boolean	noiseFlag	Create a noise cluster, otherwise assign to the nearest cluster.
double	rate	Outlier rate.

Fields inherited from class elki.clustering.kmeans.AbstractKMeans

distance, initializer, k, maxiter

Fields inherited from interface elki.clustering.kmeans.KMeans

DISTANCE_FUNCTION_ID, INIT_ID, K_ID, MAXITER_ID, SEED_ID, VARSTAT_ID

Constructor Summary

Constructors

Constructor	Description
KMeansMinusMinus(NumberVectorDistance<? super V> distance, int k, int maxiter, KMeansInitialization initializer, double rate, boolean noiseFlag)	Constructor.

Method Summary

Modifier and Type	Method	Description
protected Logging	getLogger()	Get the (STATIC) logger for this class.
Clustering<KMeansModel>	run(Relation<V> relation)	Run the clustering algorithm.

Methods inherited from class elki.clustering.kmeans.AbstractKMeans

getDistance, getInputTypeRestriction, incrementalUpdateMean, initialMeans, means, minusEquals, nearestMeans, plusEquals, plusMinusEquals, setDistance, setInitializer, setK

Methods inherited from class java.lang.Object

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

Methods inherited from interface elki.clustering.ClusteringAlgorithm

autorun

Field Detail

LOG

private static final Logging LOG

The logger for this class.

rate

public double rate

Outlier rate.

noiseFlag

public boolean noiseFlag

Create a noise cluster, otherwise assign to the nearest cluster.

Constructor Detail

KMeansMinusMinus

public KMeansMinusMinus(NumberVectorDistance<? super V> distance,
                        int k,
                        int maxiter,
                        KMeansInitialization initializer,
                        double rate,
                        boolean noiseFlag)

Constructor.

Parameters:

distance - distance function

k - k parameter

maxiter - Maxiter parameter

initializer - Initialization method

noiseFlag - Create a noise cluster instead of assigning to the nearest cluster

Method Detail

run

public Clustering<KMeansModel> run(Relation<V> relation)

Description copied from interface: KMeans

Run the clustering algorithm.

Parameters:

relation - Relation to process.

Returns:

Clustering result

getLogger

protected Logging getLogger()

Description copied from class: AbstractKMeans

Get the (STATIC) logger for this class.

Specified by:

getLogger in class AbstractKMeans<V extends NumberVector,KMeansModel>

Returns:

the static logger