| Package | Description |
|---|---|
| de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans |
K-means clustering and variations.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization |
Initialization strategies for k-means.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.parallel |
Parallelized implementations of k-means.
|
| de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain |
Clustering algorithms for uncertain data.
|
| de.lmu.ifi.dbs.elki.algorithm.outlier.clustering |
Clustering based outlier detection.
|
| tutorial.clustering |
Classes from the tutorial on implementing a custom k-means variation.
|
| Class and Description |
|---|
| AbstractKMeans
Abstract base class for k-means implementations.
|
| AbstractKMeans.Parameterizer
Parameterization class.
|
| BestOfMultipleKMeans
Run K-Means multiple times, and keep the best run.
|
| CLARA
Clustering Large Applications (CLARA) is a clustering method for large data
sets based on PAM, partitioning around medoids (
KMedoidsPAM) based on
sampling. |
| KMeans
Some constants and options shared among kmeans family algorithms.
|
| KMeansBatchedLloyd
An algorithm for k-means, using Lloyd-style bulk iterations.
|
| KMeansBisecting
The bisecting k-means algorithm works by starting with an initial
partitioning into two clusters, then repeated splitting of the largest
cluster to get additional clusters.
|
| KMeansElkan
Elkan's fast k-means by exploiting the triangle inequality.
|
| KMeansHamerly
Hamerly's fast k-means by exploiting the triangle inequality.
|
| KMeansHybridLloydMacQueen
A hybrid k-means algorithm, alternating between MacQueen-style incremental
processing and Lloyd-Style batch steps.
|
| KMeansLloyd
The standard k-means algorithm, using Lloyd-style bulk iterations.
|
| KMeansMacQueen
The original k-means algorithm, using MacQueen style incremental updates;
making this effectively an "online" (streaming) algorithm.
|
| KMediansLloyd
k-medians clustering algorithm, but using Lloyd-style bulk iterations instead
of the more complicated approach suggested by Kaufman and Rousseeuw (see
KMedoidsPAM instead). |
| KMedoidsEM
A k-medoids clustering algorithm, implemented as EM-style bulk algorithm.
|
| KMedoidsPAM
The original PAM algorithm or k-medoids clustering, as proposed by Kaufman
and Rousseeuw in "Partitioning Around Medoids".
|
| KMedoidsPAM.Parameterizer
Parameterization class.
|
| SingleAssignmentKMeans
Pseudo-k-Means variations, that assigns each object to the nearest center.
|
| XMeans
X-means: Extending K-means with Efficient Estimation on the Number of
Clusters.
|
| Class and Description |
|---|
| KMeans
Some constants and options shared among kmeans family algorithms.
|
| Class and Description |
|---|
| AbstractKMeans
Abstract base class for k-means implementations.
|
| AbstractKMeans.Parameterizer
Parameterization class.
|
| KMeans
Some constants and options shared among kmeans family algorithms.
|
| Class and Description |
|---|
| KMeans
Some constants and options shared among kmeans family algorithms.
|
| Class and Description |
|---|
| KMeans
Some constants and options shared among kmeans family algorithms.
|
| Class and Description |
|---|
| AbstractKMeans
Abstract base class for k-means implementations.
|
| KMeans
Some constants and options shared among kmeans family algorithms.
|
Copyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.