Package | Description |
---|---|
de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan |
Generalized DBSCAN
Generalized DBSCAN is an abstraction of the original DBSCAN idea,
that allows the use of arbitrary "neighborhood" and "core point" predicates.
|
de.lmu.ifi.dbs.elki.algorithm.outlier.anglebased |
Angle-based outlier detection algorithms.
|
de.lmu.ifi.dbs.elki.algorithm.outlier.clustering |
Clustering based outlier detection.
|
de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic |
Outlier detection algorithms based on intrinsic dimensionality.
|
de.lmu.ifi.dbs.elki.algorithm.outlier.lof |
LOF family of outlier detection algorithms
|
de.lmu.ifi.dbs.elki.algorithm.projection |
Data projections (see also preprocessing filters for basic projections).
|
de.lmu.ifi.dbs.elki.algorithm.statistics |
Statistical analysis algorithms.
|
de.lmu.ifi.dbs.elki.datasource.filter.normalization.columnwise |
Normalizations operating on columns / variates; where each column is treated independently.
|
de.lmu.ifi.dbs.elki.datasource.filter.transform |
Data space transformations
|
de.lmu.ifi.dbs.elki.evaluation.clustering |
Evaluation of clustering results
|
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.geometry |
Algorithms from computational geometry
|
de.lmu.ifi.dbs.elki.math.statistics.dependence |
Statistical measures of dependence, such as correlation
|
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator |
Estimators for statistical distributions.
|
de.lmu.ifi.dbs.elki.math.statistics.tests |
Statistical tests
|
de.lmu.ifi.dbs.elki.parallel.processor |
Processor API of ELKI, and some essential shared processors.
|
de.lmu.ifi.dbs.elki.utilities.scaling |
Scaling functions: linear, logarithmic, gamma, clipping, ...
|
Class and Description |
---|
MeanVariance
Do some simple statistics (mean, variance) using a numerically stable online
algorithm.
|
Class and Description |
---|
DoubleMinMax
Class to find the minimum and maximum double values in data.
|
MeanVariance
Do some simple statistics (mean, variance) using a numerically stable online
algorithm.
|
Class and Description |
---|
DoubleMinMax
Class to find the minimum and maximum double values in data.
|
Class and Description |
---|
DoubleMinMax
Class to find the minimum and maximum double values in data.
|
Class and Description |
---|
DoubleMinMax
Class to find the minimum and maximum double values in data.
|
Class and Description |
---|
Mean
Compute the mean using a numerically stable online algorithm.
|
Class and Description |
---|
DoubleMinMax
Class to find the minimum and maximum double values in data.
|
Class and Description |
---|
MeanVariance
Do some simple statistics (mean, variance) using a numerically stable online
algorithm.
|
Class and Description |
---|
MeanVarianceMinMax
Class collecting mean, variance, minimum and maximum statistics.
|
Class and Description |
---|
MeanVariance
Do some simple statistics (mean, variance) using a numerically stable online
algorithm.
|
Class and Description |
---|
Mean
Compute the mean using a numerically stable online algorithm.
|
Class and Description |
---|
DoubleMinMax
Class to find the minimum and maximum double values in data.
|
IntegerMinMax
Class to find the minimum and maximum int values in data.
|
Mean
Compute the mean using a numerically stable online algorithm.
|
MeanVariance
Do some simple statistics (mean, variance) using a numerically stable online
algorithm.
|
MeanVarianceMinMax
Class collecting mean, variance, minimum and maximum statistics.
|
SinCosTable
Class to precompute / cache Sinus and Cosinus values.
|
StatisticalMoments
Track various statistical moments, including mean, variance, skewness and
kurtosis.
|
Class and Description |
---|
DoubleMinMax
Class to find the minimum and maximum double values in data.
|
Class and Description |
---|
SinCosTable
Class to precompute / cache Sinus and Cosinus values.
|
Class and Description |
---|
DoubleMinMax
Class to find the minimum and maximum double values in data.
|
MeanVariance
Do some simple statistics (mean, variance) using a numerically stable online
algorithm.
|
StatisticalMoments
Track various statistical moments, including mean, variance, skewness and
kurtosis.
|
Class and Description |
---|
MeanVariance
Do some simple statistics (mean, variance) using a numerically stable online
algorithm.
|
Class and Description |
---|
DoubleMinMax
Class to find the minimum and maximum double values in data.
|
Class and Description |
---|
DoubleMinMax
Class to find the minimum and maximum double values in data.
|
Copyright © 2019 ELKI Development Team. License information.