R: Cluster analysis

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Cluster analysis in R


Help files with alias or concept or title matching 'cluster' using fuzzy

Kmeans(amap)               K-Means Clustering
hcluster(amap)             Hierarchical Clustering
hclusterpar(amap)          Parallelized Hierarchical Clustering
                           Cluster threshold an array.
icc(aod)                   Intra-Cluster Correlation
sscsample(Bolstad)         Simple, Stratified and Cluster Sampling
sscsample.data(Bolstad)    A stratified and clustered data set
                           Clusters Peaks of Mass Spectra
clustIndex(cclust)         Cluster Indexes
cclust(cclust)             Convex Clustering
predict.cclust(cclust)     Assign clusters to new data
cl_boot(clue)              Bootstrap Resampling of Clustering Algorithms
cl_ensemble(clue)          Cluster Ensembles
cl_pclust(clue)            Prototype-Based Partitions of Clusterings
agnes(cluster)             Agglomerative Nesting (Hierarchical Clustering)
bannerplot(cluster)        Plot Banner (of Hierarchical Clustering)
clara(cluster)             Clustering Large Applications
clara.object(cluster)      Clustering Large Applications (CLARA) Object
clusplot(cluster)          Cluster Plot - Generic Function
                           Bivariate Cluster Plot (clusplot) Default Method
meanabsdev(cluster)        Internal cluster functions
diana(cluster)             DIvisive ANAlysis Clustering
fanny(cluster)             Fuzzy Analysis Clustering
mona(cluster)              MONothetic Analysis Clustering of Binary Variables
plot.agnes(cluster)        Plots of an Agglomerative Hierarchical Clustering
plot.diana(cluster)        Plots of a Divisive Hierarchical Clustering
plot.mona(cluster)         Banner of Monothetic Divisive Hierarchical
pltree(cluster)            Clustering Trees - Generic Function
pltree.twins(cluster)      Clustering Tree of a Hierarchical Clustering
silhouette(cluster)        Compute or Extract Silhouette Information from
twins.object(cluster)      Hierarchical Clustering Object
xclara(cluster)            Bivariate Data Set with 3 Clusters
DCluster(DCluster)         A package for the detection of spatial clusters of
                           diseases for count data
besagnewell(DCluster)      Besag and Newell's statistic for spatial clustering
                           Besag and Newell's statistic for spatial clustering
bn.iscluster(DCluster)     Clustering function for Besag and Newell's method
kn.iscluster(DCluster)     Clustering function for Kulldorff and Nagarwalla's
kullnagar(DCluster)        Kulldorff and Nagarwalla's statistic for spatial
kullnagar.stat(DCluster)   Kulldorff and Nagarwalla's statistic for spatial
                           Local clustering test function
tango(DCluster)            Tango's statistic for general clustering
tango.stat(DCluster)       Compute Tango's statistic for general clustering
robcov(Design)             Robust Covariance Matrix Estimates
bclust(e1071)              Bagged Clustering
boxplot.bclust(e1071)      Boxplot of cluster profiles
cmeans(e1071)              Fuzzy C-Means Clustering
cshell(e1071)              Fuzzy C-Shell Clustering
fclustIndex(e1071)         Fuzzy Cluster Indexes (Validity/Performance
bestMclust(edci)           Choose 'best' clusters
circMclust(edci)           Circular Clustering
deldupMclust(edci)         Delete duplicate found clusters
edgecluster(edci)          Edge detection in noisy images
oregMclust(edci)           Orthogonal Regression Clustering
energy.hclust(energy)      Hierarchical Clustering by Minimum (Energy)
clusters(evd)              Identify Clusters of Exceedences
decluster(evir)            Decluster Point Process
dclust(extRemes)           Decluster data by runs declustering.
bisearch(extRemes)         extRemes internal and secondary functions
decluster(fExtremes)       fExtremes Builtin Functions
                           Preprocessing Extreme Value Data
cclust(flexclust)          Convex Clustering
kcca(flexclust)            K-Centroids Cluster Analysis
                           Predict Cluster Membership
qtclust(flexclust)         QT Clustering
ExNclus(flexmix)           Artificial Example for Normal Clustering
FLXmclust(flexmix)         FlexMix Clustering Demo Driver
flexmix-class(flexmix)     Class "flexmix"
can(fpc)                   Generation of the tuning constant for regression
                           fixed point clusters
clusexpect(fpc)            Expected value of the number of times a fixed point
                           cluster is found
cluster.stats(fpc)         Cluster validation statistics
cmahal(fpc)                Generation of tuning constant for Mahalanobis fixed
                           point clusters.
fixmahal(fpc)              Mahalanobis Fixed Point Clusters
fixreg(fpc)                Linear Regression Fixed Point Clusters
fpclusters(fpc)            Extracting clusters from fixed point cluster objects
itnumber(fpc)              Number of regression fixed point cluster iterations
mahalconf(fpc)             Mahalanobis fixed point clusters initial
minsize(fpc)               Minimum size of regression fixed point cluster
plotcluster(fpc)           Discriminant projection plot.
rFace(fpc)                 "Face-shaped" clustered benchmark datasets
regmix(fpc)                Mixture Model ML for Clusterwise Linear Regression
simmatrix(fpc)             Extracting intersections between clusters from
pfc(gap)                   Probability of familial clustering of disease
pfc.sim(gap)               Probability of familial clustering of disease
ac(gclus)                  Clustering coefficients from package cluster.
diameter(gclus)            Cluster heterogeneity of 2-d data
reorder.hclust(gclus)      Reorders object order of hclust, keeping objects
                           within a cluster contiguous to each other.
order.single(gclus)        Orders objects using hierarchical clustering
order.clusters(gclus)      Orders clustered objects using hierarchical
ordgee(geepack)            GEE for Clustered Ordinal Responses
respdis(geepack)           Clustered Ordinal Respiratory Disorder
ncclust(GeneNT)            Network constrained clustering
tdclust(GeneNT)            Traditional clustering
deff(Hmisc)                Design Effect and Intra-cluster Correlation
t.test.cluster(Hmisc)      t-test for Clustered Data
varclus(Hmisc)             Variable Clustering
bootplot(hopach)           function to make a barplot of bootstrap estimated
                           cluster membership probabilities
boothopach(hopach)         functions to perform non-parametric bootstrap
                           resampling of hopach clustering results
dplot(hopach)              function to make a pseudo-color image of a distance
                           matrix with the row and column ordering based on
                           HOPACH clustering results.
hopach(hopach)             function to perform HOPACH hierarchical clustering
collap(hopach)             Functions used internally by the hopach package
impute.knn(impute)         A function to impute missing expression data
specc(kernlab)             Spectral Clustering
EDAM(klaR)                 Computation of an Eight Direction Arranged Map
shardsplot(klaR)           Plotting Eight Direction Arranged Maps or
                           Self-Organizing Maps
consensus(maanova)         Build consensus tree out of bootstrap cluster result
JS(maanova)                Internal maanova functions
macluster(maanova)         Clustering analysis for Micro Array experiment
draw.clust(maptree)        Graph a Hierarchical Cluster Tree
group.clust(maptree)       Observation Groups for a Hierarchical Cluster Tree
kgs(maptree)               KGS Measure for Pruning Hierarchical Clusters
prune.clust(maptree)       Prunes a Hierarchical Cluster Tree
EMclust(mclust)            BIC for Model-Based Clustering
EMclustN(mclust)           BIC for Model-Based Clustering with Poisson Noise
Mclust(mclust)             Model-Based Clustering
hc(mclust)                 Model-based Hierarchical Clustering
hcE(mclust)                Model-based Hierarchical Clustering
[.mclustDAtest(mclust)     Internal MCLUST functions
plot.Mclust(mclust)        Plot Model-Based Clustering Results
uncerPlot(mclust)          Uncertainty Plot for Model-Based Clustering
trclcomp(mvpart)           Tree-Clustering Comparison
pamr.makeclasses(pamr)     A function to interactively define classes from a
                           clustering tree
pan(pan)                   Imputation of multivariate panel or cluster data
pan.bd(pan)                Imputation of multivariate panel or cluster data
NNclean(prabclus)          Nearest neighbor based clutter/noise detection
cluspop.nb(prabclus)       Simulation of presence-absence matrices (clustered)
comp.test(prabclus)        Compare species clustering and species groups
distratio(prabclus)        Distance ratio test statistics for distance based
homogen.test(prabclus)     Classical distance-based test for homogeneity
                           against clustering
pop.sim(prabclus)          p-value simulation for presence-absence matrices
                           clustering test
prabclust(prabclus)        Clustering of species ranges from presence-absence
prabtest(prabclus)         Parametric bootstrap test for clustering in
                           presence-absence matrices
randpop.nb(prabclus)       Simulation of presence-absence matrices
pvclust(pvclust)           Calculating P-values for Hierchical Clustering
pvpick(pvclust)            Find Clusters with High/Low P-values
KMeans(Rcmdr)              K-Means Clustering Using Multiple Random Seeds
assignCluster(Rcmdr)       Append a Cluster Membership Variable to a Dataframe
                           Rcmdr Hierarchical Clustering Dialog
threetwo.dat(Rfwdmv)       Three clusters two outliers
stars(rrcov)               Data for the Hertzsprung-Russell Diagram of the star
                           cluster CYG OB1
caha(SAGx)                 Clustering Goodness measured by the
                           Calinski-Harabasz index
cluster.q(SAGx)            Clustering Goodness measured by Q2
fom(SAGx)                  Clustering Figure of Merit
gap(SAGx)                  GAP statistic clustering figure of merit
myclus(SAGx)               A clustering function
                           Balanced cluster
equiv.clust(sna)           Find Clusters of Positions Based on an Equivalence
gclust.boxstats(sna)       Plot Statistics Associated with Graph Clusters
gclust.centralgraph(sna)   Get Central Graphs Associated with Graph Clusters
rMatClust(spatstat)        Simulate Matern Cluster Process
Kenv.pcp(splancs)          Calculate simulation envelope for a Poisson Cluster
pcp(splancs)               Fit a Poisson cluster process
pcp.sim(splancs)           Generate a Poisson Cluster Process
stdiagn(splancs)           Summary plots for clustering analysis
stmctest(splancs)          Monte-Carlo test of space-time clustering
stsecal(splancs)           Standard error for space-time clustering
stvmat(splancs)            Variance matrix for space-time clustering
cophenetic(stats)          Cophenetic Distances for a Hierarchical Clustering
hclust(stats)              Hierarchical Clustering
identify.hclust(stats)     Identify Clusters in a Dendrogram
kmeans(stats)              K-Means Clustering
rect.hclust(stats)         Draw Rectangles Around Hierarchical Clusters
dlda(supclust)             Classification with Wilma's Clusters
wilma(supclust)            Supervised Clustering of Predictor Variables
surveyoptions(survey)      Options for the survey package
cluster(survival)          Identify Clusters
                           Initialize a cluster of workstations
ordihull(vegan)            Add Graphical Items to Ordination Diagrams



Dear R-listers,

Is anyone familiar with a package that would perform bootstrapping on 
species/site matrices for clustering ? So far I have been using the 
vegan package to generate trees (Bray-Curtis index), but I would like 
to associate a certainty to each node (similarly to a phylogenetic 
If no package exist, I would know how to generate bootstrapped matrices 
of distance, but how could I plot the results ? Graphically, I am 
looking for something similar to what is available from the pvclust 
package (my understanding is that pvclust makes covariance clustering 
from dataframes, and it cannot be used with matrices to generate 
Bray-Curtis similarity dendrograms?)

Please forgive my ignorance!

Thank you in advance,
eric pante