de.lmu.ifi.dbs.elki.index.preprocessed.fastoptics

Class RandomProjectedNeighborsAndDensities<V extends NumberVector>

java.lang.Object

de.lmu.ifi.dbs.elki.index.preprocessed.fastoptics.RandomProjectedNeighborsAndDensities<V>

@Reference(authors="J. Schneider and M. Vlachos",
           title="Fast parameterless density-based clustering via random projections",
           booktitle="Proc. 22nd ACM international conference on Conference on Information & Knowledge Management (CIKM)",
           url="http://dx.doi.org/10.1145/2505515.2505590")
public class RandomProjectedNeighborsAndDensities<V extends NumberVector>
extends Object

Random Projections used for computing neighbors and density estimates. This index is specialized for the algorithm FastOPTICS Reference:

J. Schneider and M. Vlachos
Fast parameterless density-based clustering via random projections
Proc. 22nd ACM international conference on Conference on Information & Knowledge Management (CIKM)

This is based on the original code provided by Johannes Schneider, with ELKIfications and optimizations by Erich Schubert. TODO: implement one of the Index APIs?

Author:

Johannes Schneider, Erich Schubert

Nested Class Summary

Nested Classes

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

Field Summary

Fields

Modifier and Type	Field and Description
(package private) long	distanceComputations Count the number of distance computations.
private static Logging	LOG Class logger.
private static int	logOProjectionConst Default constant used to compute number of projections as well as number of splits of point set, ie. constant *log N*d
(package private) int	minSplitSize minimum size for which a point set is further partitioned (roughly corresponds to minPts in OPTICS)
(package private) Relation<V>	points entire point set
private static String	PREFIX Statistics logging prefix.
(package private) DoubleDataStore[]	projectedPoints all projected points
(package private) RandomFactory	rnd Random factory.
private static float	sizeTolerance Sets used for neighborhood computation should be about minSplitSize Sets are still used if they deviate by less (1+/- sizeTolerance)
(package private) ArrayList<ArrayDBIDs>	splitsets sets that resulted from recursive split of entire point set

Constructor Summary

Constructors

Constructor and Description
RandomProjectedNeighborsAndDensities(RandomFactory rnd) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
DoubleDataStore	computeAverageDistInSet() Compute for each point a density estimate as inverse of average distance to a point in a projected set
void	computeSetsBounds(Relation<V> points, int minSplitSize, DBIDs ptList) Create random projections, project points and put points into sets of size about minSplitSize/2
DataStore<? extends DBIDs>	getNeighs() Compute list of neighbors for each point from sets resulting from projection
void	logStatistics() Log some statistics.
int	splitByDistance(ArrayModifiableDBIDs ind, int begin, int end, DoubleDataStore tpro, Random rand) Split the data set by distances.
int	splitRandomly(ArrayModifiableDBIDs ind, int begin, int end, DoubleDataStore tpro, Random rand) Split the data set randomly.
void	splitupNoSort(ArrayModifiableDBIDs ind, int begin, int end, int dim, Random rand) Recursively splits entire point set until the set is below a threshold

Methods inherited from class java.lang.Object

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

Field Detail

LOG

private static final Logging LOG

Class logger.

PREFIX

private static final String PREFIX

Statistics logging prefix.

logOProjectionConst

private static final int logOProjectionConst

Default constant used to compute number of projections as well as number of splits of point set, ie. constant *log N*d

See Also:

Constant Field Values

sizeTolerance

private static final float sizeTolerance

Sets used for neighborhood computation should be about minSplitSize Sets are still used if they deviate by less (1+/- sizeTolerance)

See Also:

Constant Field Values

minSplitSize

int minSplitSize

minimum size for which a point set is further partitioned (roughly corresponds to minPts in OPTICS)

points

Relation<V extends NumberVector> points

entire point set

splitsets

ArrayList<ArrayDBIDs> splitsets

sets that resulted from recursive split of entire point set

projectedPoints

DoubleDataStore[] projectedPoints

all projected points

rnd

RandomFactory rnd

Random factory.

distanceComputations

long distanceComputations

Count the number of distance computations.

Constructor Detail

RandomProjectedNeighborsAndDensities

public RandomProjectedNeighborsAndDensities(RandomFactory rnd)

Constructor.

Parameters:

rnd - Random factory.

Method Detail

computeSetsBounds

public void computeSetsBounds(Relation<V> points,
                     int minSplitSize,
                     DBIDs ptList)

Create random projections, project points and put points into sets of size about minSplitSize/2

Parameters:

points - points to process

minSplitSize - minimum size for which a point set is further partitioned (roughly corresponds to minPts in OPTICS)

ptList - Points that are to be projected

splitupNoSort

public void splitupNoSort(ArrayModifiableDBIDs ind,
                 int begin,
                 int end,
                 int dim,
                 Random rand)

Recursively splits entire point set until the set is below a threshold

Parameters:

ind - points that are in the current set

begin - Interval begin in the ind array

end - Interval end in the ind array

dim - depth of projection (how many times point set has been split already)

rand - Random generator

splitRandomly

public int splitRandomly(ArrayModifiableDBIDs ind,
                int begin,
                int end,
                DoubleDataStore tpro,
                Random rand)

Split the data set randomly.

Parameters:

ind - Object index

begin - Interval begin

end - Interval end

tpro - Projection

rand - Random generator

Returns:

Splitting point

splitByDistance

public int splitByDistance(ArrayModifiableDBIDs ind,
                  int begin,
                  int end,
                  DoubleDataStore tpro,
                  Random rand)

Split the data set by distances.

Parameters:

ind - Object index

begin - Interval begin

end - Interval end

tpro - Projection

rand - Random generator

Returns:

Splitting point

getNeighs

public DataStore<? extends DBIDs> getNeighs()

Compute list of neighbors for each point from sets resulting from projection

Returns:

list of neighbors for each point

computeAverageDistInSet

public DoubleDataStore computeAverageDistInSet()

Compute for each point a density estimate as inverse of average distance to a point in a projected set

Returns:

for each point average distance to point in a set

logStatistics

public void logStatistics()

Log some statistics.