de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans

Class KMeansMinusMinus<V extends NumberVector>

java.lang.Object

de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm<R>

de.lmu.ifi.dbs.elki.algorithm.AbstractNumberVectorDistanceBasedAlgorithm<V,Clustering<M>>

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.AbstractKMeans<V,KMeansModel>

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansMinusMinus<V>

Type Parameters:

V - vector datatype

All Implemented Interfaces:

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

@Title(value="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 and Description
protected class	KMeansMinusMinus.Instance Inner instance, storing state for a single data set.
static class	KMeansMinusMinus.Parameterizer<V extends NumberVector> Parameterization class.

Field Summary

Fields

Modifier and Type	Field and 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 de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.AbstractKMeans

initializer, k, maxiter

Fields inherited from class de.lmu.ifi.dbs.elki.algorithm.AbstractNumberVectorDistanceBasedAlgorithm

distanceFunction

Fields inherited from class de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm

ALGORITHM_ID

Fields inherited from interface de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeans

INIT_ID, K_ID, MAXITER_ID, SEED_ID, VARSTAT_ID

Fields inherited from interface de.lmu.ifi.dbs.elki.algorithm.DistanceBasedAlgorithm

DISTANCE_FUNCTION_ID

Constructor Summary

Constructors

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

Method Summary

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

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.AbstractKMeans

getInputTypeRestriction, incrementalUpdateMean, initialMeans, means, minusEquals, nearestMeans, plusEquals, plusMinusEquals, setDistanceFunction, setInitializer, setK

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.AbstractNumberVectorDistanceBasedAlgorithm

getDistanceFunction

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm

run

Methods inherited from class java.lang.Object

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

Methods inherited from interface de.lmu.ifi.dbs.elki.algorithm.clustering.ClusteringAlgorithm

run

Methods inherited from interface de.lmu.ifi.dbs.elki.algorithm.DistanceBasedAlgorithm

getDistanceFunction

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(NumberVectorDistanceFunction<? super V> distanceFunction,
                        int k,
                        int maxiter,
                        KMeansInitialization initializer,
                        double rate,
                        boolean noiseFlag)

Constructor.

Parameters:

distanceFunction - 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(Database database,
                                   Relation<V> relation)

Description copied from interface: KMeans

Run the clustering algorithm.

Parameters:

database - Database to run on.

relation - Relation to process.

Returns:

Clustering result

getLogger

protected Logging getLogger()

Description copied from class: AbstractAlgorithm

Get the (STATIC) logger for this class.

Specified by:

getLogger in class AbstractAlgorithm<Clustering<KMeansModel>>

Returns:

the static logger