# Clustering Algorithms for Bioconductor

## Documentation for package ‘bluster’ version 1.19.1

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

.defaultScalarArguments | Define the default arguments
.defaultScalarArguments-method | Agglomerative nesting
.defaultScalarArguments-method | Clustering Large Applications
.defaultScalarArguments-method | Divisive analysis clustering
.defaultScalarArguments-method | Hierarchical clustering
.defaultScalarArguments-method | The HierarchicalParam class
.defaultScalarArguments-method | Partitioning around medoids
.defaultScalarArguments-method | Define the default arguments
.extractScalarArguments | Define the default arguments
.showScalarArguments | Define the default arguments
AffinityParam | Affinity propogation
AffinityParam-class | Affinity propogation
AgnesParam | Agglomerative nesting
AgnesParam-class | Agglomerative nesting
approxSilhouette | Approximate silhouette width
BlusterParam-class | The BlusterParam class
bootstrapStability | Assess cluster stability by bootstrapping
centers | The FixedNumberParam class
centers-method | The FixedNumberParam class
centers<- | The FixedNumberParam class
centers<--method | The FixedNumberParam class
ClaraParam | Clustering Large Applications
ClaraParam-class | Clustering Large Applications
clusterRMSD | Compute the RMSD per cluster
clusterRows | Cluster rows of a matrix
clusterRows-method | Affinity propogation
clusterRows-method | Agglomerative nesting
clusterRows-method | Clustering Large Applications
clusterRows-method | Density-based clustering with DBSCAN
clusterRows-method | Divisive analysis clustering
clusterRows-method | Dirichlet multinomial mixture clustering
clusterRows-method | Hierarchical clustering
clusterRows-method | K-means clustering
clusterRows-method | Mini-batch k-means clustering
clusterRows-method | Graph-based clustering
clusterRows-method | Partitioning around medoids
clusterRows-method | Clustering with self-organizing maps
clusterRows-method | Two step clustering with vector quantization
clusterSweep | Clustering parameter sweeps
compareClusterings | Compare pairs of clusterings
DbscanParam | Density-based clustering with DBSCAN
DbscanParam-class | Density-based clustering with DBSCAN
DianaParam | Divisive analysis clustering
DianaParam-class | Divisive analysis clustering
DmmParam | Dirichlet multinomial mixture clustering
DmmParam-class | Dirichlet multinomial mixture clustering
FixedNumberParam-class | The FixedNumberParam class
HclustParam | Hierarchical clustering
HclustParam-class | Hierarchical clustering
HierarchicalParam-class | The HierarchicalParam class
KmeansParam | K-means clustering
KmeansParam-class | K-means clustering
KNNGraphParam | Graph-based clustering
KNNGraphParam-class | Graph-based clustering
linkClusters | Create a graph between different clusterings
linkClustersMatrix | Create a graph between different clusterings
makeKNNGraph | Build a nearest-neighbor graph
makeSNNGraph | Build a nearest-neighbor graph
MbkmeansParam | Mini-batch k-means clustering
MbkmeansParam-class | Mini-batch k-means clustering
mergeCommunities | Merge communities from graph-based clustering
neighborPurity | Compute neighborhood purity
neighborsToKNNGraph | Build a nearest-neighbor graph
neighborsToSNNGraph | Build a nearest-neighbor graph
nestedClusters | Map nested clusterings
NNGraphParam | Graph-based clustering
NNGraphParam-class | Graph-based clustering
pairwiseModularity | Compute pairwise modularity
pairwiseRand | Compute pairwise Rand indices
PamParam | Partitioning around medoids
PamParam-class | Partitioning around medoids
show-method | Affinity propogation
show-method | Agglomerative nesting
show-method | The BlusterParam class
show-method | Clustering Large Applications
show-method | Density-based clustering with DBSCAN
show-method | Divisive analysis clustering
show-method | Dirichlet multinomial mixture clustering
show-method | The FixedNumberParam class
show-method | Hierarchical clustering
show-method | The HierarchicalParam class
show-method | K-means clustering
show-method | Mini-batch k-means clustering
show-method | Graph-based clustering
show-method | Partitioning around medoids
show-method | Clustering with self-organizing maps
show-method | Two step clustering with vector quantization
SNNGraphParam | Graph-based clustering
SNNGraphParam-class | Graph-based clustering
SomParam | Clustering with self-organizing maps
SomParam-class | Clustering with self-organizing maps
TwoStepParam | Two step clustering with vector quantization
TwoStepParam-class | Two step clustering with vector quantization
updateObject-method | Hierarchical clustering
updateObject-method | K-means clustering
[[-method | The BlusterParam class
[[-method | Hierarchical clustering
[[<--method | The BlusterParam class