V
- vector datatype@Title(value="Compare-Means") @Reference(authors="S. J. Phillips", title="Acceleration of k-means and related clustering algorithms", booktitle="Proc. 4th Int. Workshop on Algorithm Engineering and Experiments (ALENEX 2002)", url="http://dx.doi.org/10.1007/3-540-45643-0_13") public class KMeansCompare<V extends NumberVector> extends AbstractKMeans<V,KMeansModel>
S. J. Phillips
Acceleration of k-means and related clustering algorithms
Proc. 4th Int. Workshop on Algorithm Engineering and Experiments (ALENEX
2002)
Modifier and Type | Class and Description |
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static class |
KMeansCompare.Parameterizer<V extends NumberVector>
Parameterization class.
|
Modifier and Type | Field and Description |
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private static String |
KEY
Key for statistics logging.
|
private static Logging |
LOG
The logger for this class.
|
initializer, k, maxiter
distanceFunction
INIT_ID, K_ID, MAXITER_ID, SEED_ID
DISTANCE_FUNCTION_ID
Constructor and Description |
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KMeansCompare(NumberVectorDistanceFunction<? super V> distanceFunction,
int k,
int maxiter,
KMeansInitialization<? super V> initializer)
Constructor.
|
Modifier and Type | Method and Description |
---|---|
private boolean |
assignToNearestCluster(Relation<V> relation,
List<Vector> means,
List<ModifiableDBIDs> clusters,
WritableIntegerDataStore assignment,
double[] varsum,
double[][] cdist,
LongStatistic diststat)
Reassign objects, but only if their bounds indicate it is necessary to do
so.
|
protected Logging |
getLogger()
Get the (STATIC) logger for this class.
|
private void |
recomputeSeperation(List<Vector> means,
double[][] cdist,
LongStatistic diststat)
Recompute the separation of cluster means.
|
Clustering<KMeansModel> |
run(Database database,
Relation<V> relation)
Run the clustering algorithm.
|
assignToNearestCluster, getInputTypeRestriction, incrementalUpdateMean, logVarstat, macQueenIterate, means, medians, setDistanceFunction, setK
getDistanceFunction
makeParameterDistanceFunction, run
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
run
getDistanceFunction
private static final Logging LOG
private static final String KEY
public KMeansCompare(NumberVectorDistanceFunction<? super V> distanceFunction, int k, int maxiter, KMeansInitialization<? super V> initializer)
distanceFunction
- distance functionk
- k parametermaxiter
- Maxiter parameterinitializer
- Initialization methodpublic Clustering<KMeansModel> run(Database database, Relation<V> relation)
KMeans
database
- Database to run on.relation
- Relation to process.private void recomputeSeperation(List<Vector> means, double[][] cdist, LongStatistic diststat)
means
- Meanscdist
- Center-to-Center distancesdiststat
- Distance counting statisticprivate boolean assignToNearestCluster(Relation<V> relation, List<Vector> means, List<ModifiableDBIDs> clusters, WritableIntegerDataStore assignment, double[] varsum, double[][] cdist, LongStatistic diststat)
relation
- Datameans
- Current meansclusters
- Current clustersassignment
- Cluster assignmentvarsum
- Variance sum countercdist
- Centroid distancesdiststat
- Distance statisticsprotected Logging getLogger()
AbstractAlgorithm
getLogger
in class AbstractAlgorithm<Clustering<KMeansModel>>
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