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

Interface KMeans<V extends NumberVector,M extends Model>

Type Parameters:

V - Number vector type

M - Actual model type

All Superinterfaces:

Algorithm, ClusteringAlgorithm<Clustering<M>>, DistanceBasedAlgorithm<V>

All Known Implementing Classes:

AbstractKMeans, BestOfMultipleKMeans, KMeansAnnulus, KMeansBisecting, KMeansCompare, KMeansElkan, KMeansExponion, KMeansHamerly, KMeansLloyd, KMeansMacQueen, KMeansMinusMinus, KMeansSimplifiedElkan, KMeansSort, KMediansLloyd, ParallelLloydKMeans, SameSizeKMeansAlgorithm, SingleAssignmentKMeans, XMeans

public interface KMeans<V extends NumberVector,M extends Model>
extends ClusteringAlgorithm<Clustering<M>>, DistanceBasedAlgorithm<V>

Some constants and options shared among kmeans family algorithms.

Since:

0.1

Author:

Erich Schubert

Field Summary

Fields

Modifier and Type	Field and Description
static OptionID	INIT_ID Parameter to specify the initialization method
static OptionID	K_ID Parameter to specify the number of clusters to find, must be an integer greater than 0.
static OptionID	MAXITER_ID Parameter to specify the number of clusters to find, must be an integer greater or equal to 0, where 0 means no limit.
static OptionID	SEED_ID Parameter to specify the random generator seed.
static OptionID	VARSTAT_ID Flag to compute the final clustering variance statistic (for methods that employ bounds to avoid computing all distances).

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

DISTANCE_FUNCTION_ID

Method Summary

Modifier and Type	Method and Description
Clustering<M>	run(Database database, Relation<V> rel) Run the clustering algorithm.
void	setDistanceFunction(NumberVectorDistanceFunction<? super V> distanceFunction) Set the distance function to use.
void	setInitializer(KMeansInitialization init) Set the initialization method.
void	setK(int k) Set the value of k.

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

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

getInputTypeRestriction

Field Detail

INIT_ID

static final OptionID INIT_ID

Parameter to specify the initialization method

K_ID

static final OptionID K_ID

Parameter to specify the number of clusters to find, must be an integer greater than 0.

MAXITER_ID

static final OptionID MAXITER_ID

Parameter to specify the number of clusters to find, must be an integer greater or equal to 0, where 0 means no limit.

SEED_ID

static final OptionID SEED_ID

Parameter to specify the random generator seed.

VARSTAT_ID

static final OptionID VARSTAT_ID

Flag to compute the final clustering variance statistic (for methods that employ bounds to avoid computing all distances).

Method Detail

run

Clustering<M> run(Database database,
                  Relation<V> rel)

Run the clustering algorithm.

Parameters:

database - Database to run on.

rel - Relation to process.

Returns:

Clustering result

setK

void setK(int k)

Set the value of k. Needed for some types of nested k-means.

Parameters:

k - K parameter

setDistanceFunction

void setDistanceFunction(NumberVectorDistanceFunction<? super V> distanceFunction)

Set the distance function to use.

Parameters:

distanceFunction - Distance function.

setInitializer

void setInitializer(KMeansInitialization init)

Set the initialization method.

Parameters:

init - Initialization method