de.lmu.ifi.dbs.elki.algorithm.clustering.correlation
Class HiCO<V extends NumberVector<V,?>>

java.lang.Object
  de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm<R>
      de.lmu.ifi.dbs.elki.algorithm.AbstractDistanceBasedAlgorithm<O,D,ClusterOrderResult<D>>
          de.lmu.ifi.dbs.elki.algorithm.clustering.OPTICS<V,PCACorrelationDistance>
              de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.HiCO<V>

Type Parameters:

V - the type of NumberVector handled by the algorithm

All Implemented Interfaces:

Algorithm, OPTICSTypeAlgorithm<PCACorrelationDistance>, InspectionUtilFrequentlyScanned, Parameterizable

@Title(value="Mining Hierarchies of Correlation Clusters")
@Description(value="Algorithm for detecting hierarchies of correlation clusters.")
@Reference(authors="E. Achtert, C. B\u00f6hm, P. Kr\u00f6ger, A. Zimek",
           title="Mining Hierarchies of Correlation Clusterse",
           booktitle="Proc. Int. Conf. on Scientific and Statistical Database Management (SSDBM\'06), Vienna, Austria, 2006",
           url="http://dx.doi.org/10.1109/SSDBM.2006.35")
public class HiCO<V extends NumberVector<V,?>>
extends OPTICS<V,PCACorrelationDistance>

Implementation of the HiCO algorithm, an algorithm for detecting hierarchies of correlation clusters.

Reference: E. Achtert, C. Böhm, P. Kröger, A. Zimek: Mining Hierarchies of Correlation Clusters.
In: Proc. Int. Conf. on Scientific and Statistical Database Management (SSDBM'06), Vienna, Austria, 2006.

Nested Class Summary

static class

HiCO.Parameterizer<V extends NumberVector<V,?>> Parameterization class.

Field Summary

static OptionID	ALPHA_ID The threshold for 'strong' eigenvectors: the 'strong' eigenvectors explain a portion of at least alpha of the total variance.
static double	DEFAULT_ALPHA The default value for ALPHA_ID.
static double	DEFAULT_DELTA The default value for DELTA_ID.
static OptionID	DELTA_ID Parameter to specify the threshold of a distance between a vector q and a given space that indicates that q adds a new dimension to the space, must be a double equal to or greater than 0.
static OptionID	K_ID Optional parameter to specify the number of nearest neighbors considered in the PCA, must be an integer greater than 0.
private static Logging	logger The logger for this class.
static OptionID	MU_ID Parameter to specify the smoothing factor, must be an integer greater than 0.

Fields inherited from class de.lmu.ifi.dbs.elki.algorithm.clustering.OPTICS

EPSILON_ID, MINPTS_ID

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

DISTANCE_FUNCTION_ID

Constructor Summary

HiCO(PCABasedCorrelationDistanceFunction distanceFunction, int mu)
Constructor.

Method Summary

protected Logging

getLogger() Get the (STATIC) logger for this class.

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

expandClusterOrder, expandClusterOrderDouble, getDistanceFactory, getInputTypeRestriction, getMinPts, run

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

getDistanceFunction

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

makeParameterDistanceFunction, 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.OPTICSTypeAlgorithm

run

Field Detail

logger

private static final Logging logger

The logger for this class.

MU_ID

public static final OptionID MU_ID

Parameter to specify the smoothing factor, must be an integer greater than 0. The {link MU_ID-nearest neighbor is used to compute the correlation reachability of an object.

Key: -hico.mu

K_ID

public static final OptionID K_ID

Optional parameter to specify the number of nearest neighbors considered in the PCA, must be an integer greater than 0. If this parameter is not set, k is set to the value of MU_ID.

Key: -hico.k

Default value: MU_ID

DELTA_ID

public static final OptionID DELTA_ID

Parameter to specify the threshold of a distance between a vector q and a given space that indicates that q adds a new dimension to the space, must be a double equal to or greater than 0.

Default value: 0.25

Key: -hico.delta

ALPHA_ID

public static final OptionID ALPHA_ID

The threshold for 'strong' eigenvectors: the 'strong' eigenvectors explain a portion of at least alpha of the total variance.

Default value: DEFAULT_ALPHA

Key: -hico.alpha

DEFAULT_DELTA

public static final double DEFAULT_DELTA

The default value for DELTA_ID.

See Also:

Constant Field Values

DEFAULT_ALPHA

public static final double DEFAULT_ALPHA

The default value for ALPHA_ID.

See Also:

Constant Field Values

Constructor Detail

HiCO

public HiCO(PCABasedCorrelationDistanceFunction distanceFunction,
            int mu)

Constructor.

Parameters:

distanceFunction - Distance function

mu - Mu parameter

Method Detail

getLogger

protected Logging getLogger()

Description copied from class: AbstractAlgorithm

Get the (STATIC) logger for this class.

Overrides:

getLogger in class OPTICS<V extends NumberVector<V,?>,PCACorrelationDistance>

Returns:

the static logger