Package | Description |
---|---|
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans |
K-means clustering and variations.
|
de.lmu.ifi.dbs.elki.index.preprocessed.knn |
Indexes providing KNN and rKNN data.
|
de.lmu.ifi.dbs.elki.math |
Mathematical operations and utilities used throughout the framework.
|
de.lmu.ifi.dbs.elki.math.dimensionsimilarity |
Functions to compute the similarity of dimensions (or the interestingness of the combination).
|
Modifier and Type | Method and Description |
---|---|
protected boolean |
KMedoidsEM.assignToNearestCluster(ArrayDBIDs means,
Mean[] mdist,
List<? extends ModifiableDBIDs> clusters,
DistanceQuery<V> distQ)
Returns a list of clusters.
|
Modifier and Type | Field and Description |
---|---|
(package private) Mean |
SpacefillingKNNPreprocessor.mean
Mean number of distance computations
|
(package private) Mean |
SpacefillingMaterializeKNNPreprocessor.mean
Mean number of distance computations
|
(package private) Mean |
NaiveProjectedKNNPreprocessor.mean
Mean number of distance computations.
|
Modifier and Type | Class and Description |
---|---|
class |
MeanVariance
Do some simple statistics (mean, variance) using a numerically stable online
algorithm.
|
class |
MeanVarianceMinMax
Class collecting mean, variance, minimum and maximum statistics.
|
class |
StatisticalMoments
Track various statistical moments, including mean, variance, skewness and
kurtosis.
|
Modifier and Type | Method and Description |
---|---|
static Mean[] |
Mean.newArray(int dimensionality)
Create and initialize a new array of MeanVariance
|
Mean |
Mean.put(double[] vals)
Add values with weight 1.0
|
Mean |
Mean.put(double[] vals,
double[] weights)
Add values with weight 1.0
|
Modifier and Type | Method and Description |
---|---|
void |
Mean.put(Mean other)
Join the data of another MeanVariance instance.
|
void |
MeanVariance.put(Mean other)
Join the data of another MeanVariance instance.
|
void |
StatisticalMoments.put(Mean other)
Join the data of another MeanVariance instance.
|
void |
MeanVarianceMinMax.put(Mean other) |
Constructor and Description |
---|
Mean(Mean other)
Constructor from other instance
|
Modifier and Type | Method and Description |
---|---|
private void |
MCEDimensionSimilarity.divide(DBIDArrayIter it,
double[] data,
ArrayList<DBIDs> idx,
int start,
int end,
int depth,
Mean mean)
Recursive call to further subdivide the array.
|
Copyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.