| Package | Description |
|---|---|
| de.lmu.ifi.dbs.elki.algorithm |
Algorithms suitable as a task for the
KDDTask main routine. |
| de.lmu.ifi.dbs.elki.algorithm.benchmark |
Benchmarking pseudo algorithms.
|
| de.lmu.ifi.dbs.elki.algorithm.classification |
Classification algorithms.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering |
Clustering algorithms.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan |
Generalized DBSCAN.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical |
Hierarchical agglomerative clustering (HAC).
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans |
K-means clustering and variations.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.optics |
OPTICS family of clustering algorithms.
|
| de.lmu.ifi.dbs.elki.algorithm.outlier |
Outlier detection algorithms
|
| de.lmu.ifi.dbs.elki.algorithm.outlier.clustering |
Clustering based outlier detection.
|
| de.lmu.ifi.dbs.elki.algorithm.outlier.distance |
Distance-based outlier detection algorithms, such as DBOutlier and kNN.
|
| de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel |
Parallel implementations of distance-based outlier detectors.
|
| de.lmu.ifi.dbs.elki.algorithm.outlier.lof |
LOF family of outlier detection algorithms.
|
| de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel |
Parallelized variants of LOF.
|
| de.lmu.ifi.dbs.elki.algorithm.outlier.spatial |
Spatial 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.)
|
| tutorial.clustering |
Classes from the tutorial on implementing a custom k-means variation.
|
| tutorial.outlier |
| Modifier and Type | Class and Description |
|---|---|
class |
KNNDistancesSampler<O>
Provides an order of the kNN-distances for all objects within the database.
|
class |
KNNJoin<V extends NumberVector,N extends SpatialNode<N,E>,E extends SpatialEntry>
Joins in a given spatial database to each object its k-nearest neighbors.
|
class |
MaterializeDistances<O>
Algorithm to materialize all the distances in a data set.
|
| Modifier and Type | Class and Description |
|---|---|
class |
KNNBenchmarkAlgorithm<O>
Benchmarking algorithm that computes the k nearest neighbors for each query
point.
|
class |
RangeQueryBenchmarkAlgorithm<O extends NumberVector>
Benchmarking algorithm that computes a range query for each point.
|
class |
ValidateApproximativeKNNIndex<O>
Algorithm to validate the quality of an approximative kNN index, by
performing a number of queries and comparing them to the results obtained by
exact indexing (e.g. linear scanning).
|
| Modifier and Type | Class and Description |
|---|---|
class |
KNNClassifier<O>
KNNClassifier classifies instances based on the class distribution among the
k nearest neighbors in a database.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CanopyPreClustering<O>
Canopy pre-clustering is a simple preprocessing step for clustering.
|
class |
DBSCAN<O>
Density-Based Clustering of Applications with Noise (DBSCAN), an algorithm to
find density-connected sets in a database.
|
class |
NaiveMeanShiftClustering<V extends NumberVector>
Mean-shift based clustering algorithm.
|
| Modifier and Type | Class and Description |
|---|---|
class |
LSDBC<O extends NumberVector>
Locally scaled Density Based Clustering.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractHDBSCAN<O,R extends Result>
Abstract base class for HDBSCAN variations.
|
class |
AGNES<O>
Hierarchical Agglomerative Clustering (HAC) or Agglomerative Nesting (AGNES)
is a classic hierarchical clustering algorithm.
|
class |
AnderbergHierarchicalClustering<O>
This is a modification of the classic AGNES algorithm for hierarchical
clustering using a nearest-neighbor heuristic for acceleration.
|
class |
CLINK<O>
CLINK algorithm for complete linkage.
|
class |
HDBSCANLinearMemory<O>
Linear memory implementation of HDBSCAN clustering.
|
class |
SLINK<O>
Implementation of the efficient Single-Link Algorithm SLINK of R.
|
class |
SLINKHDBSCANLinearMemory<O>
Linear memory implementation of HDBSCAN clustering based on SLINK.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CLARA<V>
Clustering Large Applications (CLARA) is a clustering method for large data
sets based on PAM, partitioning around medoids (
KMedoidsPAM) based on
sampling. |
class |
KMedoidsEM<V>
A k-medoids clustering algorithm, implemented as EM-style bulk algorithm.
|
class |
KMedoidsPAM<V>
The original PAM algorithm or k-medoids clustering, as proposed by Kaufman
and Rousseeuw in "Partitioning Around Medoids".
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractOPTICS<O>
The OPTICS algorithm for density-based hierarchical clustering.
|
class |
DeLiClu<NV extends NumberVector>
DeliClu: Density-Based Hierarchical Clustering, a hierarchical algorithm to
find density-connected sets in a database.
|
class |
OPTICSHeap<O>
The OPTICS algorithm for density-based hierarchical clustering.
|
class |
OPTICSList<O>
The OPTICS algorithm for density-based hierarchical clustering.
|
| Modifier and Type | Class and Description |
|---|---|
class |
COP<V extends NumberVector>
Correlation outlier probability: Outlier Detection in Arbitrarily Oriented
Subspaces
Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek
Outlier Detection in Arbitrarily Oriented Subspaces in: Proc. |
class |
DWOF<O>
Algorithm to compute dynamic-window outlier factors in a database based on a
specified parameter
DWOF.Parameterizer.K_ID (-dwof.k). |
class |
OPTICSOF<O>
Optics-OF outlier detection algorithm, an algorithm to find Local Outliers in
a database based on ideas from
OPTICSTypeAlgorithm clustering. |
class |
SimpleCOP<V extends NumberVector>
Algorithm to compute local correlation outlier probability.
|
| Modifier and Type | Class and Description |
|---|---|
class |
SilhouetteOutlierDetection<O>
Outlier detection by using the Silhouette Coefficients.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractDBOutlier<O>
Simple distance based outlier detection algorithms.
|
class |
DBOutlierDetection<O>
Simple distanced based outlier detection algorithm.
|
class |
DBOutlierScore<O>
Compute percentage of neighbors in the given neighborhood with size d.
|
class |
HilOut<O extends NumberVector>
Fast Outlier Detection in High Dimensional Spaces
Outlier Detection using Hilbert space filling curves
Reference:
F.
|
class |
KNNOutlier<O>
Outlier Detection based on the distance of an object to its k nearest
neighbor.
|
class |
KNNWeightOutlier<O>
Outlier Detection based on the accumulated distances of a point to its k
nearest neighbors.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ParallelKNNOutlier<O>
Parallel implementation of KNN Outlier detection.
|
class |
ParallelKNNWeightOutlier<O>
Parallel implementation of KNN Weight Outlier detection.
|
| Modifier and Type | Class and Description |
|---|---|
class |
COF<O>
Connectivity-based outlier factor (COF).
|
class |
INFLO<O>
Influence Outliers using Symmetric Relationship (INFLO) using two-way search,
is an outlier detection method based on LOF; but also using the reverse kNN.
|
class |
KDEOS<O>
Generalized Outlier Detection with Flexible Kernel Density Estimates.
|
class |
LDF<O extends NumberVector>
Outlier Detection with Kernel Density Functions.
|
class |
LDOF<O>
Computes the LDOF (Local Distance-Based Outlier Factor) for all objects of a
Database.
|
class |
LOCI<O>
Fast Outlier Detection Using the "Local Correlation Integral".
|
class |
LOF<O>
Algorithm to compute density-based local outlier factors in a database based
on a specified parameter
LOF.Parameterizer.K_ID (-lof.k). |
class |
SimpleKernelDensityLOF<O extends NumberVector>
A simple variant of the LOF algorithm, which uses a simple kernel density
estimation instead of the local reachability density.
|
class |
SimplifiedLOF<O>
A simplified version of the original LOF algorithm, which does not use the
reachability distance, yielding less stable results on inliers.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ParallelLOF<O>
Parallel implementation of Local Outlier Factor using processors.
|
class |
ParallelSimplifiedLOF<O>
Parallel implementation of Simplified-LOF Outlier detection using processors.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CTLuGLSBackwardSearchAlgorithm<V extends NumberVector>
GLS-Backward Search is a statistical approach to detecting spatial outliers.
|
class |
CTLuRandomWalkEC<N>
Spatial outlier detection based on random walks.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AveragePrecisionAtK<O>
Evaluate a distance functions performance by computing the average precision
at k, when ranking the objects by distance.
|
class |
DistanceStatisticsWithClasses<O>
Algorithm to gather statistics over the distance distribution in the data
set.
|
class |
EstimateIntrinsicDimensionality<O>
Estimate global average intrinsic dimensionality of a data set.
|
class |
EvaluateRankingQuality<V extends NumberVector>
Evaluate a distance function with respect to kNN queries.
|
class |
EvaluateRetrievalPerformance<O>
Evaluate a distance functions performance by computing the mean average
precision, ROC, and NN classification performance when ranking the objects by
distance.
|
class |
RankingQualityHistogram<O>
Evaluate a distance function with respect to kNN queries.
|
| Modifier and Type | Class and Description |
|---|---|
class |
NaiveAgglomerativeHierarchicalClustering1<O>
This tutorial will step you through implementing a well known clustering
algorithm, agglomerative hierarchical clustering, in multiple steps.
|
class |
NaiveAgglomerativeHierarchicalClustering2<O>
This tutorial will step you through implementing a well known clustering
algorithm, agglomerative hierarchical clustering, in multiple steps.
|
class |
NaiveAgglomerativeHierarchicalClustering3<O>
This tutorial will step you through implementing a well known clustering
algorithm, agglomerative hierarchical clustering, in multiple steps.
|
class |
NaiveAgglomerativeHierarchicalClustering4<O>
This tutorial will step you through implementing a well known clustering
algorithm, agglomerative hierarchical clustering, in multiple steps.
|
| Modifier and Type | Class and Description |
|---|---|
class |
DistanceStddevOutlier<O>
A simple outlier detection algorithm that computes the standard deviation of
the kNN distances.
|
class |
ODIN<O>
Outlier detection based on the in-degree of the kNN graph.
|
Copyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.