See: Description
| Interface | Description |
|---|---|
| KMeans<V extends NumberVector,M extends Model> |
Some constants and options shared among kmeans family algorithms.
|
| Class | Description |
|---|---|
| AbstractKMeans<V extends NumberVector,M extends Model> |
Abstract base class for k-means implementations.
|
| AbstractKMeans.Instance |
Inner instance for a run, for better encapsulation, that encapsulates the
standard flow of most (but not all) k-means variations.
|
| AbstractKMeans.Parameterizer<V extends NumberVector> |
Parameterization class.
|
| BestOfMultipleKMeans<V extends NumberVector,M extends MeanModel> |
Run K-Means multiple times, and keep the best run.
|
| BestOfMultipleKMeans.Parameterizer<V extends NumberVector,M extends MeanModel> |
Parameterization 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. |
| CLARA.CachedDistanceQuery<V> |
Cached distance query.
|
| CLARA.Parameterizer<V> |
Parameterization class.
|
| CLARANS<V> |
CLARANS: a method for clustering objects for spatial data mining
is inspired by PAM (partitioning around medoids,
KMedoidsPAM)
and CLARA and also based on sampling. |
| CLARANS.Assignment |
Assignment state.
|
| CLARANS.Parameterizer<V> |
Parameterization class.
|
| FastCLARA<V> |
Clustering Large Applications (CLARA) with the
KMedoidsFastPAM
improvements, to increase scalability in the number of clusters. |
| FastCLARA.Parameterizer<V> |
Parameterization class.
|
| FastCLARANS<V> |
A faster variation of CLARANS, that can explore O(k) as many swaps at a
similar cost by considering all medoids for each candidate non-medoid.
|
| FastCLARANS.Assignment |
Assignment state.
|
| FastCLARANS.Parameterizer<V> |
Parameterization class.
|
| KMeansAnnulus<V extends NumberVector> |
Annulus k-means algorithm.
|
| KMeansAnnulus.Instance |
Inner instance, storing state for a single data set.
|
| KMeansAnnulus.Parameterizer<V extends NumberVector> |
Parameterization class.
|
| KMeansBisecting<V extends NumberVector,M extends MeanModel> |
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.
|
| KMeansBisecting.Parameterizer<V extends NumberVector,M extends MeanModel> |
Parameterization class.
|
| KMeansCompare<V extends NumberVector> |
Compare-Means: Accelerated k-means by exploiting the triangle inequality and
pairwise distances of means to prune candidate means.
|
| KMeansCompare.Instance |
Inner instance, storing state for a single data set.
|
| KMeansCompare.Parameterizer<V extends NumberVector> |
Parameterization class.
|
| KMeansElkan<V extends NumberVector> |
Elkan's fast k-means by exploiting the triangle inequality.
|
| KMeansElkan.Instance |
Inner instance, storing state for a single data set.
|
| KMeansElkan.Parameterizer<V extends NumberVector> |
Parameterization class.
|
| KMeansExponion<V extends NumberVector> |
Newlings's exponion k-means algorithm, exploiting the triangle inequality.
|
| KMeansExponion.Instance |
Inner instance, storing state for a single data set.
|
| KMeansExponion.Parameterizer<V extends NumberVector> |
Parameterization class.
|
| KMeansHamerly<V extends NumberVector> |
Hamerly's fast k-means by exploiting the triangle inequality.
|
| KMeansHamerly.Instance |
Inner instance, storing state for a single data set.
|
| KMeansHamerly.Parameterizer<V extends NumberVector> |
Parameterization class.
|
| KMeansLloyd<V extends NumberVector> |
The standard k-means algorithm, using bulk iterations and commonly attributed
to Lloyd and Forgy (independently).
|
| KMeansLloyd.Instance |
Inner instance, storing state for a single data set.
|
| KMeansLloyd.Parameterizer<V extends NumberVector> |
Parameterization class.
|
| KMeansMacQueen<V extends NumberVector> |
The original k-means algorithm, using MacQueen style incremental updates;
making this effectively an "online" (streaming) algorithm.
|
| KMeansMacQueen.Instance |
Inner instance, storing state for a single data set.
|
| KMeansMacQueen.Parameterizer<V extends NumberVector> |
Parameterization class.
|
| KMeansMinusMinus<V extends NumberVector> |
k-means--: A Unified Approach to Clustering and Outlier Detection.
|
| KMeansMinusMinus.Parameterizer<V extends NumberVector> |
Parameterization class.
|
| KMeansSimplifiedElkan<V extends NumberVector> |
Simplified version of Elkan's k-means by exploiting the triangle inequality.
|
| KMeansSimplifiedElkan.Instance |
Inner instance, storing state for a single data set.
|
| KMeansSimplifiedElkan.Parameterizer<V extends NumberVector> |
Parameterization class.
|
| KMeansSort<V extends NumberVector> |
Sort-Means: Accelerated k-means by exploiting the triangle inequality and
pairwise distances of means to prune candidate means (with sorting).
|
| KMeansSort.Instance |
Inner instance, storing state for a single data set.
|
| KMeansSort.Parameterizer<V extends NumberVector> |
Parameterization class.
|
| KMediansLloyd<V extends NumberVector> |
k-medians clustering algorithm, but using Lloyd-style bulk iterations instead
of the more complicated approach suggested by Kaufman and Rousseeuw (see
KMedoidsPAM instead). |
| KMediansLloyd.Instance |
Inner instance, storing state for a single data set.
|
| KMediansLloyd.Parameterizer<V extends NumberVector> |
Parameterization class.
|
| KMedoidsFastPAM<V> |
FastPAM: An improved version of PAM, that is usually O(k) times faster.
|
| KMedoidsFastPAM.Instance |
Instance for a single dataset.
|
| KMedoidsFastPAM.Parameterizer<V> |
Parameterization class.
|
| KMedoidsFastPAM1<V> |
FastPAM1: A version of PAM that is O(k) times faster, i.e., now in O((n-k)²).
|
| KMedoidsFastPAM1.Instance |
Instance for a single dataset.
|
| KMedoidsFastPAM1.Parameterizer<V> |
Parameterization class.
|
| KMedoidsPAM<V> |
The original Partitioning Around Medoids (PAM) algorithm or k-medoids
clustering, as proposed by Kaufman and Rousseeuw in "Clustering by means of
Medoids".
|
| KMedoidsPAM.Instance |
Instance for a single dataset.
|
| KMedoidsPAM.Parameterizer<V> |
Parameterization class.
|
| KMedoidsPAMReynolds<V> |
The Partitioning Around Medoids (PAM) algorithm with some additional
optimizations proposed by Reynolds et al.
|
| KMedoidsPAMReynolds.Instance |
Instance for a single dataset.
|
| KMedoidsPAMReynolds.Parameterizer<V> |
Parameterization class.
|
| KMedoidsPark<V> |
A k-medoids clustering algorithm, implemented as EM-style bulk algorithm.
|
| KMedoidsPark.Parameterizer<V> |
Parameterization class.
|
| SingleAssignmentKMeans<V extends NumberVector> |
Pseudo-k-Means variations, that assigns each object to the nearest center.
|
| SingleAssignmentKMeans.Instance |
Inner instance, storing state for a single data set.
|
| SingleAssignmentKMeans.Parameterizer<V extends NumberVector> |
Parameterization class.
|
| XMeans<V extends NumberVector,M extends MeanModel> |
X-means: Extending K-means with Efficient Estimation on the Number of
Clusters.
|
| XMeans.Parameterizer<V extends NumberVector,M extends MeanModel> |
Parameterization class.
|
Copyright © 2019 ELKI Development Team. License information.