CRAN Task View: Cluster Analysis & Finite Mixture Models

This CRAN Task View contains a list of packages that can be used for finding groups in data and modelling unobserved cross-sectional 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.

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.
• Function dendrogram() from stats and associated methods can be used for improved visualization for cluster dendrograms.
• pvclust is a package for assessing the uncertainty in hierarchical cluster analysis. It provides approximately unbiased p-values as well as bootstrap p-values.
• hybridHclust implements hybrid hierarchical clustering via mutual clusters.

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 flexclust provides k-centroid 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.
• Package trimcluster provides trimmed k-means clustering.

Model-based Clustering:

• Package mclust (and for backward compatibility mclust02) 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.
• prabclus clusters a presence-absence matrix object by calculating an MDS from the distances, and applying maximum likelihood Gaussian mixtures clustering to the MDS points.
• 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.
• Mixtures of univariate normal distributions can be printed and plotted using package nor1mix. Package bayesmix provides Bayesian estimation using JAGS. Bayesian estimation using a variational approach for multivariate Gaussian distributions with a diagonal covariance matrix is provided by package vabayelMix. Robust estimation using Weighted Likelihood can be done with package wle.
• Package MFDA implements model-based functional data analysis.

Other Cluster Algorithms:

• Package amap provides alternative implementations of k-means and agglomerative hierarchical clustering.
• Package cba implements clustering techniques for business analytics like "rock" and "proximus".
• Package clue implements ensemble methods for both hierarchical and partitioning cluster methods.
• Fuzzy clustering and bagged clustering are available in package e1071.
• The hopach algorithm is a hybrid between hierarchical methods and PAM and builds a tree by recursively partitioning a data set.
• Self-organizing maps are available in package som.

Cluster-wise Regression:

• Package flexmix implements an user-extensible framework for EM-estimation of mixtures of regression models, including mixtures of (generalized) linear models.
• Package fpc provides fixed-point methods both for model-based clustering and linear regression. A collection of asymmetric projection methods can be used to plot various aspects of a clustering.
• Multigroup mixtures of latent Markov models on mixed categorical and continuous data (including time series) can be fitted using depmix. The parameters are optimized using a general purpose optimization routine given linear and nonlinear constraints on the parameters.
• Package mixreg fits mixtures of one-variable regressions and provides the bootstrap test for the number of components.
• Mixed-mode latent class regression with special focus on longitudinal data is implemented by mmlcr. The components can follow a multivariate distribution of a (censored) Gaussian, multinomial, negative binomial or Poisson distribution. In addition concomitant variables can be specified to model the priors.
• moc fits mixture models to multivariate mixed data using a Newton-type algorithm. The component specific distribution may have one, two or three parameters. Covariates and concomitant variables can be specified as well as constraints for the parameters.
• mixtools provides fitting with the EM algorithm of mixtures of multinomials, multivariate normals, normals with repeated measures, Poisson regressions and Gaussian regressions (with random effects) and with the Metropolis-Hastings algorithm of mixtures of Gaussian regressions.

CRAN packages:

• amap
• bayesm
• bayesmix
• cba
• clue
• cluster (core)
• depmix
• e1071
• flexclust (core)
• flexmix (core)
• fpc
• hopach
• hybridHclust
• mclust (core)
• MFDA
• mixreg
• mixtools
• mmlcr
• moc
• nor1mix
• prabclus
• pvclust
• som
• trimcluster
• vabayelMix
• wle

Related links:

• CRAN Task View: MachineLearning
• mixdist package for grouped or conditional data