Last updated on 20180504
by Friedrich Leisch and Bettina Gruen
This CRAN Task View contains a list of packages that can be
used for finding groups in data and modeling unobserved
crosssectional heterogeneity. Many packages provide functionality for
more than one of the topics listed below, the section headings are
mainly meant as quick starting points rather than an ultimate
categorization. Except for packages stats and cluster (which ship with
base R and hence are part of every R installation), each package is
listed only once.
Most of the packages listed in this CRAN Task View, but not all are
distributed under the GPL. Please have a look at the DESCRIPTION file
of each package to check under which license it is distributed.
Hierarchical Clustering:

Functions
hclust()
from package stats and
agnes()
from cluster are the primary
functions for agglomerative hierarchical clustering, function
diana()
can be used for divisive hierarchical
clustering. Faster alternatives to hclust()
are
provided by the packages fastcluster and
flashClust.
 Function
dendrogram()
from stats and associated
methods can be used for improved visualization for cluster
dendrograms.
 The dendextend package provides functions for easy
visualization (coloring labels and branches, etc.), manipulation
(rotating, pruning, etc.) and comparison of dendrograms (tangelgrams
with heuristics for optimal branch rotations, and tree correlation
measures with bootstrap and permutation tests for
significance).
 Package dynamicTreeCut contains methods for detection
of clusters in hierarchical clustering dendrograms.
 Package genie implements a fast hierarchical
clustering algorithm with a linkage criterion which is a variant of
the single linkage method combining it with the Gini inequality
measure to robustify the linkage method while retaining
computational efficiency to allow for the use of larger data
sets.
 hybridHclust implements hybrid hierarchical
clustering via mutual clusters.
 Package idendr0 allows to interactively explore
hierarchical clustering dendrograms and the clustered data. The data
can be visualized (and interacted with) in a builtin heat map, but
also in GGobi dynamic interactive graphics (provided by
rggobi), or base R plots.
 Package isopam uses an algorithm which is based on
the classification of ordination scores from isometric feature
mapping. The classification is performed either as a hierarchical,
divisive method or as nonhierarchical partitioning.
 The package protoclust implements a form of
hierarchical clustering that associates a prototypical element with
each interior node of the dendrogram. Using the package's
plot()
function, one can produce dendrograms that are
prototypelabeled and are therefore easier to interpret.
 pvclust is a package for assessing the uncertainty in
hierarchical cluster analysis. It provides approximately
unbiased pvalues as well as bootstrap pvalues.
 Package sparcl provides clustering for a set of
n observations when p variables are available, where
p >> n. It adaptively chooses a set of variables
to use in clustering the observations. Sparse kmeans clustering and
sparse hierarchical clustering are implemented.
Partitioning Clustering:
 Function
kmeans()
from package stats provides
several algorithms for computing partitions with respect to
Euclidean distance.
 Function
pam()
from package cluster implements
partitioning around medoids and can work with arbitrary
distances. Function clara()
is a
wrapper to pam()
for larger data sets. Silhouette plots
and spanning ellipses can be used for visualization.
 Package apcluster implements Frey's and Dueck's
Affinity Propagation clustering. The algorithms in the package are analogous
to the Matlab code published by Frey and Dueck.
 Package clusterSim allows to search for the optimal
clustering procedure for a given dataset.
 Package clustMixType implements Huang’s kprototypes
extension of kmeans for mixed type data.
 Package evclust implements various clustering
algorithms that produce a credal partition, i.e., a set of
DempsterShafer mass functions representing the membership of
objects to clusters.
 Package flexclust provides kcentroid cluster
algorithms for arbitrary distance measures, hard competitive
learning, neural gas and QT clustering. Neighborhood graphs and
image plots of partitions are available for visualization. Some of
this functionality is also provided by package cclust.
 Package kernlab provides a weighted kernel version of
the kmeans algorithm by
kkmeans
and spectral
clustering by specc
.
 Package kml provides kmeans
clustering specifically for longitudinal (joint) data.
 Package skmeans allows spherical kMeans Clustering,
i.e. kmeans clustering with cosine similarity. It features several
methods, including a genetic and a simple fixedpoint algorithm and
an interface to the CLUTO vcluster program for clustering
highdimensional datasets.
 Package trimcluster provides trimmed kmeans
clustering. Package tclust also allows for trimmed
kmeans clustering. In addition using this package other covariance
structures can also be specified for the clusters.
ModelBased Clustering:
 ML estimation:
 For semi or partially supervised problems, where for a part of
the observations labels are given with certainty or with some
probability, package bgmm provides beliefbased and
softlabel mixture modeling for mixtures of Gaussians with the EM
algorithm.
 EMCluster provides EM algorithms and several
efficient initialization methods for modelbased clustering of
finite mixture Gaussian distribution with unstructured dispersion in
unsupervised as well as semisupervised learning situation.
 Packages funHDDC and funFEM
implement modelbased functional data
analysis.
The funFEM package implements the funFEM
algorithm which allows to cluster time series or, more generally,
functional data. It is based on a discriminative functional mixture
model which allows the clustering of the data in a unique and
discriminative functional subspace. This model presents the
advantage to be parsimonious and can therefore handle long time
series.
The funHDDC package implements the funHDDC algorithm
which allows the clustering of functional data within groupspecific
functional subspaces. The funHDDC algorithm is based on a functional
mixture model which models and clusters the data into groupspecific
functional subspaces. The approach allows afterward meaningful
interpretations by looking at the groupspecific functional
curves.
 Package HDclassif provides function
hddc
to fit Gaussian mixture model to highdimensional data where it is
assumed that the data lives in a lower dimension than the original
space.
 Package teigen allows to fit multivariate
tdistribution mixture models (with eigendecomposed covariance
structure) from a clustering or classification point of
view. Package longclust allows to fit these models as
well as Gaussian mixture models to longitudinal data.
 Package mclust fits mixtures of Gaussians using the EM
algorithm. It allows fine control of volume and shape of covariance
matrices and agglomerative hierarchical clustering based on maximum
likelihood. It provides comprehensive strategies using hierarchical
clustering, EM and the Bayesian Information Criterion (BIC) for
clustering, density estimation, and discriminant analysis. Package
Rmixmod provides tools for fitting mixture models of
multivariate Gaussian or multinomial components to a given data set
with either a clustering, a density estimation or a discriminant
analysis point of view. Package mclust as well as packages
mixture and Rmixmod provide all 14 possible
variancecovariance structures based on the eigenvalue
decomposition.
 Package MetabolAnalyze fits mixtures of probabilistic
principal component analysis with the EM algorithm.
 For grouped conditional data package mixdist can be
used.
 mixtools provides fitting with the EM algorithm for
parametric and nonparametric (multivariate) mixtures. Parametric
mixtures include mixtures of multinomials, multivariate normals,
normals with repeated measures, Poisson regressions and Gaussian
regressions (with random effects). Nonparametric mixtures include
the univariate semiparametric case where symmetry is imposed for
identifiability and multivariate nonparametric mixtures with
conditional independent assumption. In addition fitting mixtures of
Gaussian regressions with the MetropolisHastings algorithm is
available.
 Fitting finite mixtures of uni and multivariate scale mixtures
of skewnormal distributions with the EM algorithm is provided by
package mixsmsn.
 Package MoEClust fits parsimonious finite
multivariate Gaussian mixtures of experts models via the EM
algorithm. Covariates may influence the mixing proportions and/or
component densities and all 14 constrained covariance
parameterizations from package mclust are
implemented.
 Package movMF fits finite mixtures of von
MisesFisher distributions with the EM algorithm.
 Package GLDEX fits mixtures of generalized lambda
distributions and for grouped conditional data package
mixdist can be used.
 mritc provides tools for classification using normal
mixture models and (higher resolution) hidden Markov normal mixture
models fitted by various methods.
 prabclus clusters a presenceabsence matrix
object by calculating an MDS
from the distances, and applying maximum likelihood Gaussian
mixtures clustering to the MDS
points.
 Package psychomix estimates mixtures of the
dichotomous Rasch model (via conditional ML) and the BradleyTerry
model. Package mixRasch estimates mixture Rasch models,
including the dichotomous Rasch model, the rating scale model, and
the partial credit model with joint maximum likelihood estimation.
 Package pmclust allows to use unsupervised
modelbased clustering for high dimensional (ultra) large data. The
package uses pbdMPI to perform a parallel version of the EM
algorithm for mixtures of Gaussians.
 Package rebmix implements the REBMIX algorithm to fit
mixtures of conditionally independent normal, lognormal, Weibull,
gamma, binomial, Poisson, Dirac or von Mises component densities as
well as mixtures of multivariate normal component densities with
unrestricted variancecovariance matrices.
 Bayesian estimation:
 Bayesian estimation of finite mixtures of multivariate Gaussians
is possible using package bayesm. The package provides
functionality for sampling from such a mixture as well as estimating
the model using Gibbs sampling. Additional functionality for
analyzing the MCMC chains is available for averaging
the moments over MCMC draws, for determining the marginal densities,
for clustering observations and for plotting the uni and bivariate
marginal densities.
 Package bayesmix provides Bayesian estimation using
JAGS.
 Package bclust allows Bayesian clustering using a
spikeandslab hierarchical model and is suitable for clustering
highdimensional data.
 Package Bmix provides Bayesian Sampling for
stickbreaking mixtures.
 Package bmixture provides Bayesian estimation of
finite mixtures of univariate Gamma and normal distributions.
 Package dpmixsim fits Dirichlet process mixture
models using conjugate models with normal structure. Package
profdpm determines the maximum posterior estimate for
product partition models where the Dirichlet process mixture is a
specific case in the class.
 Package GSM fits mixtures of gamma distributions.
 Package mixAK contains a mixture of statistical
methods including the MCMC methods to analyze normal mixtures with
possibly censored data.
 Package IMIFA fits Infinite Mixtures of Infinite
Factor Analyzers and a flexible suite of related models for
clustering highdimensional data. The number of clusters
and/or number of clusterspecific latent factors can be
nonparametrically inferred, without recourse to model selection
criteria.
 Package mcclust implements methods for processing a
sample of (hard) clusterings, e.g. the MCMC output of a Bayesian
clustering model. Among them are methods that find a single best
clustering to represent the sample, which are based on the posterior
similarity matrix or a relabeling algorithm.
 Package PReMiuM is a package for profile regression,
which is a Dirichlet process Bayesian clustering where the response
is linked nonparametrically to the covariate profile.
 Package rjags provides an interface to the JAGS
MCMC library which includes a module for mixture modelling.
 Other estimation methods:
 Package AdMit allows to fit an adaptive mixture of Studentt
distributions to approximate a target density through its kernel
function.
 Package CEC uses crossentropy clustering to
automatically remove unnecessary clusters, while at the same time
allowing the simultaneous use of various types of Gaussian mixture
models.
 Circular and orthogonal regression clustering using redescending
Mestimators is provided by package edci.
 Robust estimation using Weighted Likelihood can be done with
package wle.
Other Cluster Algorithms:
 Package ADPclust allows to cluster high dimensional
data based on a two dimensional decision plot. This densitydistance
plot plots for each data point the local density against the
shortest distance to all observations with a higher local density
value. The cluster centroids of this noniterative procedure can be
selected using an interactive or automatic selection mode.
 Package amap provides alternative implementations
of kmeans and agglomerative hierarchical clustering.
 Package biclust provides several algorithms to find
biclusters in twodimensional data.
 Package cba implements clustering techniques for
business analytics like "rock" and "proximus".
 Package CHsharp clusters 3dimensional data into
their local modes based on a convergent form of Choi and Hall's
(1999) data sharpening method.
 Package clue implements ensemble methods for both
hierarchical and partitioning cluster methods.
 Package CoClust implements a cluster algorithm that
is based on copula functions and therefore allows to group
observations according to the multivariate dependence structure of
the generating process without any assumptions on the margins.
 Fuzzy clustering and bagged clustering are available in package
e1071. Further and more extensive tools for fuzzy
clustering are available in package fclust.
 Package compHclust provides complimentary
hierarchical clustering which was especially designed for microarray
data to uncover structures present in the data that arise from
'weak' genes.
 Package dbscan provides a fast reimplementation of
the DBSCAN (densitybased spatial clustering of applications with
noise) algorithm using a kdtree.
 Package FactoClass performs a combination of
factorial methods and cluster analysis.
 The hopach algorithm is a hybrid between
hierarchical methods and PAM and builds a tree by
recursively partitioning a data set.
 Package largeVis implements the algorithm of the same
name for visualizing very large highdimensional datasets. Regarding
clustering optimized implementations of the HDBSCAN*, DBSCAN and
OPTICS algorithms are provided in combination with a very fast search
for approximate nearest neighbors and outlier detection.
 For graphs and networks modelbased clustering approaches are
implemented in latentnet.
 Package optpart contains a set of algorithms for
creating partitions and coverings of objects largely based on
operations on similarity relations (or matrices).
 Package pdfCluster provides tools to perform cluster
analysis via kernel density estimation. Clusters are associated to
the maximally connected components with estimated density above a
threshold. In addition a tree structure associated with the
connected components is obtained.
 Package prcr implements the 2step cluster analysis
where first hierarchical clustering is performed to determine the
initial partition for the subsequent kmeans clustering
procedure.
 Package randomLCA provides the fitting of latent
class models which optionally also include a random effect. Package
poLCA allows for polytomous variable latent class
analysis and regression. BayesLCA allows to fit Bayesian
LCA models employing the EM algorithm, Gibbs sampling or variational
Bayes methods.
 Package RPMM fits recursively partitioned mixture
models for Beta and Gaussian Mixtures. This is a modelbased
clustering algorithm that returns a hierarchy of classes, similar to
hierarchical clustering, but also similar to finite mixture
models.
 Selforganizing maps are available in package
som.
 Several packages provide cluster algorithms which have been
developed for bioinformatics applications. These packages include
FunCluster for profiling microarray expression data
and ORIClust
for orderrestricted informationbased clustering.
Clusterwise Regression:
 Multigroup mixtures of latent Markov models on mixed categorical
and continuous data (including time series) can be fitted using
depmix or depmixS4. The parameters are
optimized using a general purpose optimization routine given linear
and nonlinear constraints on the parameters.
 Package flexCWM allows for maximum likelihood fitting of
clusterweighted models, a class of mixtures of regression models
with random covariates.
 Package flexmix implements an userextensible
framework for EMestimation of mixtures of regression models,
including mixtures of (generalized) linear models.
 Package fpc provides fixedpoint methods both for
modelbased clustering and linear regression. A collection of
asymmetric projection methods can be used to plot various
aspects of a clustering.
 Package lcmm fits a latent class linear mixed model
which is also known as growth mixture model or heterogeneous linear
mixed model using a maximum likelihood method.
 Package mixreg fits mixtures of onevariable
regressions and provides the bootstrap test for the number of
components.
 mixPHM fits mixtures of proportional hazard models
with the EM algorithm.
 Package gamlss.mx fits
finite mixtures of gamlss family distributions.
Additional Functionality:
 Mixtures of univariate normal distributions can be printed
and plotted using package nor1mix.
 Package clusterfly allows
to visualize the results of clustering algorithms.
 Package clusterGeneration contains functions for
generating random clusters and random covariance/correlation
matrices, calculating a separation index (data and population
version) for pairs of clusters or cluster distributions, and 1D and
2D projection plots to visualize clusters.
Alternatively MixSim generates a finite mixture model
with Gaussian components for prespecified levels of maximum and/or
average overlaps. This model can be used to simulate data for
studying the performance of cluster algorithms.
 For cluster validation package clusterRepro tests the
reproducibility of a cluster. Package clv contains
popular internal and external cluster validation methods ready to
use for most of the outputs produced by functions from package
cluster and clValid calculates several
stability measures.
 Package clustvarsel provides variable selection for
Gaussian modelbased clustering. Variable selection for latent
class analysis for clustering multivariate categorical data is
implemented in package LCAvarsel.
Package VarSelLCM provides variable selection for
modelbased clustering of continuous, count, categorical or
mixedtype data with missing values where the models used impose a
conditional independence assumption given group membership.
 Functionality to compare the similarity between two cluster
solutions is provided by
cluster.stats()
in package
fpc.
 The stability of kcentroid clustering solutions fitted using
functions from package flexclust can also be validated
via
bootFlexclust()
using bootstrap methods.
 Package MOCCA provides methods to analyze cluster
alternatives based on multiobjective optimization of cluster
validation indices.
 Package NbClust implements 30 different indices which
evaluate the cluster structure and should help to determine on a
suitable number of clusters.
 Package seriation provides
dissplot()
for
visualizing dissimilarity matrices using seriation and matrix shading.
This also allows to inspect cluster quality by restricting objects
belonging to the same cluster to be displayed in consecutive order.
 Package sigclust provides a statistical method for
testing the significance of clustering results.
 Package treeClust calculates dissimilarities
between data points based on their leaf memberships in regression or
classification trees for each variable. It also performs the cluster
analysis using the resulting dissimilarity matrix with available
heuristic clustering algorithms in R.