| Package | Description |
|---|---|
| de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans |
K-means clustering and variations.
|
| de.lmu.ifi.dbs.elki.algorithm.outlier |
Outlier detection algorithms
|
| de.lmu.ifi.dbs.elki.algorithm.outlier.lof |
LOF family of outlier detection algorithms.
|
| de.lmu.ifi.dbs.elki.algorithm.statistics |
Statistical analysis algorithms
The algorithms in this package perform statistical analysis of the data
(e.g. compute distributions, distance distributions etc.)
|
| de.lmu.ifi.dbs.elki.data |
Basic classes for different data types, database object types and label types.
|
| de.lmu.ifi.dbs.elki.datasource.filter.normalization |
Data normalization.
|
| de.lmu.ifi.dbs.elki.evaluation.clustering |
Evaluation of clustering results.
|
| 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).
|
| de.lmu.ifi.dbs.elki.math.geometry |
Algorithms from computational geometry.
|
| 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.utilities.datastructures.histogram |
Classes for computing histograms.
|
| de.lmu.ifi.dbs.elki.utilities.scaling |
Scaling functions: linear, logarithmic, gamma, clipping, ...
|
| Class and Description |
|---|
| Mean
Compute the mean using a numerically stable online algorithm.
|
| 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.
|
| 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 |
|---|
| 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.
|
| 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.
|
| MinMax
Class to find the minimum and maximum double values in data.
|
| SinCosTable
Class to precompute / cache Sinus and Cosinus values.
|
| StatisticalMoments
Track various statistical moments, including mean, variance, skewness and
kurtosis.
|
| Class and Description |
|---|
| Mean
Compute the mean using a numerically stable online algorithm.
|
| SinCosTable
Class to precompute / cache Sinus and Cosinus values.
|
| 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.
|
| 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 |
|---|
| 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.
|