de.lmu.ifi.dbs.elki.algorithm.statistics

Class HopkinsStatisticClusteringTendency

java.lang.Object

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

de.lmu.ifi.dbs.elki.algorithm.AbstractNumberVectorDistanceBasedAlgorithm<NumberVector,Result>

de.lmu.ifi.dbs.elki.algorithm.statistics.HopkinsStatisticClusteringTendency

All Implemented Interfaces:

Algorithm, DistanceBasedAlgorithm<NumberVector>

@Reference(authors="B. Hopkins and J. G. Skellam",
           title="A new method for determining the type of distribution of plant individuals",
           booktitle="Annals of Botany, 18(2), 213-227",
           url="http://aob.oxfordjournals.org/content/18/2/213.short")
public class HopkinsStatisticClusteringTendency
extends AbstractNumberVectorDistanceBasedAlgorithm<NumberVector,Result>

The Hopkins Statistic of Clustering Tendency measures the probability that a data set is generated by a uniform data distribution. The statistic compares the ratio of the 1NN distance for objects from the data set compared to the 1NN distances of uniform distributed objects. Reference:

B. Hopkins and J. G. Skellam
A new method for determining the type of distribution of plant individuals
Annals of Botany, 18(2), 213-227.

Author:

Lisa Reichert, Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	HopkinsStatisticClusteringTendency.Parameterizer Parameterization class.

Field Summary

Fields

Modifier and Type	Field and Description
protected int	k Nearest neighbor to use.
private static Logging	LOG The logger for this class.
private double[]	maxima Stores the maximum in each dimension.
private double[]	minima Stores the minimum in each dimension.
protected RandomFactory	random Random generator seeding.
protected int	rep Number of repetitions
protected int	sampleSize The parameter sampleSizes

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

distanceFunction

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

DISTANCE_FUNCTION_ID

Constructor Summary

Constructors

Constructor and Description
HopkinsStatisticClusteringTendency(NumberVectorDistanceFunction<? super NumberVector> distanceFunction, int samplesize, RandomFactory random, int rep, int k, double[] minima, double[] maxima) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
protected double	computeNNForRealData(KNNQuery<NumberVector> knnQuery, Relation<NumberVector> relation, int dim) Search nearest neighbors for real data members.
protected double	computeNNForUniformData(KNNQuery<NumberVector> knnQuery, double[] min, double[] extend) Search nearest neighbors for artificial, uniform data.
TypeInformation[]	getInputTypeRestriction() Get the input type restriction used for negotiating the data query.
protected Logging	getLogger() Get the (STATIC) logger for this class.
protected void	initializeDataExtends(Relation<NumberVector> relation, int dim, double[] min, double[] extend) Initialize the uniform sampling area.
Result	run(Database database, Relation<NumberVector> relation) Runs the algorithm in the timed evaluation part.

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

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.Algorithm

run

Field Detail

LOG

private static final Logging LOG

The logger for this class.

sampleSize

protected int sampleSize

The parameter sampleSizes

rep

protected int rep

Number of repetitions

k

protected int k

Nearest neighbor to use.

random

protected RandomFactory random

Random generator seeding.

maxima

private double[] maxima

Stores the maximum in each dimension.

minima

private double[] minima

Stores the minimum in each dimension.

Constructor Detail

HopkinsStatisticClusteringTendency

public HopkinsStatisticClusteringTendency(NumberVectorDistanceFunction<? super NumberVector> distanceFunction,
                                  int samplesize,
                                  RandomFactory random,
                                  int rep,
                                  int k,
                                  double[] minima,
                                  double[] maxima)

Constructor.

Parameters:

distanceFunction - Distance function

samplesize - Sample size

random - Random generator

rep - Number of repetitions

k - Nearest neighbor to use

minima - Data space minima, may be null (get from data).

maxima - Data space minima, may be null (get from data).

Method Detail

run

public Result run(Database database,
         Relation<NumberVector> relation)

Runs the algorithm in the timed evaluation part.

Parameters:

database - Database context

relation - Relation to analyze

computeNNForRealData

protected double computeNNForRealData(KNNQuery<NumberVector> knnQuery,
                          Relation<NumberVector> relation,
                          int dim)

Search nearest neighbors for real data members.

Parameters:

knnQuery - KNN query

relation - Data relation

Returns:

Aggregated 1NN distances

computeNNForUniformData

protected double computeNNForUniformData(KNNQuery<NumberVector> knnQuery,
                             double[] min,
                             double[] extend)

Search nearest neighbors for artificial, uniform data.

Parameters:

knnQuery - KNN query

min - Data minima

extend - Data extend

Returns:

Aggregated 1NN distances

initializeDataExtends

protected void initializeDataExtends(Relation<NumberVector> relation,
                         int dim,
                         double[] min,
                         double[] extend)

Initialize the uniform sampling area.

Parameters:

relation - Data relation

dim - Dimensionality

min - Minima output array (preallocated!)

extend - Data extend output array (preallocated!)

getLogger

protected Logging getLogger()

Description copied from class: AbstractAlgorithm

Get the (STATIC) logger for this class.

Specified by:

getLogger in class AbstractAlgorithm<Result>

Returns:

the static logger

getInputTypeRestriction

public TypeInformation[] getInputTypeRestriction()

Description copied from class: AbstractAlgorithm

Get the input type restriction used for negotiating the data query.

Specified by:

getInputTypeRestriction in interface Algorithm

Specified by:

getInputTypeRestriction in class AbstractAlgorithm<Result>

Returns:

Type restriction