Type:	Package
Encoding:	UTF-8
Title:	Robust Trimmed Clustering
Version:	2.2-0
VersionNote:	Released 2.1-2 on 2025-06-29 on CRAN
Maintainer:	Valentin Todorov <valentin@todorov.at>
Description:	Provides functions for robust trimmed clustering. The methods are described in Garcia-Escudero (2008) <doi:10.1214/07-AOS515>, Fritz et al. (2012) <doi:10.18637/jss.v047.i12>, Garcia-Escudero et al. (2011) <doi:10.1007/s11222-010-9194-z> and others.
Depends:	R(≥ 3.6.2)
Imports:	Rcpp (≥ 1.0.7), doParallel, parallel, foreach, MASS, rlang, methods
Suggests:	mclust, cluster, sn
LazyLoad:	yes
License:	GPL-3
LinkingTo:	Rcpp, RcppArmadillo
NeedsCompilation:	yes
RoxygenNote:	7.3.3
URL:	https://github.com/valentint/tclust
BugReports:	https://github.com/valentint/tclust/issues
Repository:	CRAN
Author:	Valentin Todorov [aut, cre], Luis Angel García Escudero [aut], Agustín Mayo Iscar [aut], Javier Crespo Guerrero [aut], Heinrich Fritz [aut]
Packaged:	2026-04-25 20:39:00 UTC; valen
Date/Publication:	2026-04-26 10:20:03 UTC

Discriminant Factor analysis for tclust objects

Description

Analyzes a tclust-object by calculating discriminant factors and comparing the quality of the actual cluster assignments to that of the second best possible assignment for each observation. Cluster assignments of observations with large discriminant factors are considered "doubtful" decisions. Silhouette plots give a graphical overview of the discriminant factors distribution (see plot.DiscrFact). More details can be found in García-Escudero et al. (2011).

Usage

DiscrFact(x, threshold = 1/10)

Arguments

Value

The function returns an S3 object of type DiscrFact containing the following components:

• x A tclust object.

• ylimmin A minimum y-limit calculated for plotting purposes.

• ind The actual cluster assignment.

• ind2 The second most likely cluster assignment for each observation.

• lik The (weighted) likelihood of the actual cluster assignment of each observation.

• lik2 The (weighted) likelihood of the second best cluster assignment of each observation.

• assignfact The factor log(disc/disc2).

• threshold The threshold used for deciding whether assignfact indicates a "doubtful" assignment.

• mean.DiscrFact A vector of length k + 1 containing the mean discriminant factors for each cluster (including the outliers).

References

García-Escudero, L.A.; Gordaliza, A.; Matrán, C. and Mayo-Iscar, A. (2011), "Exploring the number of groups in robust model-based clustering." Statistics and Computing, 21 pp. 585-599, <doi:10.1007/s11222-010-9194-z>

Computes the Fowlkes and Mallows index

Description

Fowlkes-Mallows index is an external evaluation method that is used to determine the similarity between two clusterings (clusters obtained after a clustering algorithm). This measure of similarity could be either between two hierarchical clusterings or a clustering and a benchmark classification. A higher the value for the Fowlkes-Mallows index indicates a greater similarity between the clusters and the benchmark classifications. This index can be used to compare either two cluster label sets or a cluster label set with a true label set. The formula of the adjusted Fowlkes-Mallows index (ABk) is given in the details.

Usage

FowlkesMallowsIndex(c1, c2 = NULL, noisecluster = NULL)

Arguments

Details

The formula of the adjusted Fowlkes-Mallows index (ABk) is as follows:

ABk=\frac{Bk-Expected~value~of~Bk}{Max~Index - Expected~value~of~Bk}

Value

A list containing the following components:

References

Fowlkes, E.B. and Mallows, C.L. (1983), A Method for Comparing Two Hierarchical Clusterings, Journal of the American Statistical Association, Vol. 78, pp. 553–569.

See Also

randIndex

Examples

 ##  1. FowlkesMallowsIndex (adjusted) with the two vectors as input
 c <- matrix(c(1, 1, 1, 2, 2, 1,2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3), 
         ncol=2, byrow=TRUE)

 ##  c1 - numeric vector containing the labels of the first partition
 c1 <- c[, 1]

 ##  c2 - numeric vector containing the labels of the second partition
 c2 <- c[, 2]

 (FM <- FowlkesMallowsIndex(c1, c2))

 ##  2. FM index (adjusted) with the contingency table as input.
 T <- matrix(c(1, 1, 0, 1, 2, 1, 0, 0, 4), ncol=3, byrow=TRUE)
 (FM <- FowlkesMallowsIndex(T))


 ##  3. Compare FM (unadjusted) for iris data (true classification against 
 ##      tclust classification).

 ##  First partition c1 is the true partition
 c1 <- iris$Species

 ##  Second partition c2 is the output of tclust clustering procedure
 out <- tclust(iris[, 1:4], k=3, alpha=0, restr.fact=100)
 c2<- out$cluster

 (FM <- FowlkesMallowsIndex(c1, c2))

 ##  4. Compare FM index (unadjusted) for iris data (exclude unassigned units from tclust).

 ##  First partition c1 is the true partition
 c1 <- iris$Species

 ##  Second partition c2 is the output of tclust clustering procedure
 out <- tclust(iris[, 1:4], k=3, alpha=0.1, restr.fact=100)
 c2<- out$cluster

 ##  Units inside c2 which contain number 0 are referred to trimmed observations
 noisecluster <- 0
 (FM <- FowlkesMallowsIndex(c1, c2, noisecluster=noisecluster))

LG5data data

Description

A data set in dimension 10 with three clusters around affine subspaces of common intrinsic dimension. A 10% background noise is added uniformly distributed in a rectangle containing the three main clusters.

Usage

data(LG5data)

Format

The first 10 columns are the variables. The last column is the true classification vector where symbol "0" stands for the contaminating data points.

Examples

#--- EXAMPLE 1 ------------------------------------------ 
data (LG5data)
x <- LG5data[, 1:10]
clus <- rlg(x, d = c(2,2,2), alpha=0.1, trace=TRUE)
plot(x, col=clus$cluster+1)

M5data data

Description

A bivariate data set obtained from three normal bivariate distributions with different scales and proportions 1:2:2. One of the components is very overlapped with another one. A 10% background noise is added uniformly distributed in a rectangle containing the three normal components and not very overlapped with the three mixture components. A precise description of the M5 data set can be found in García-Escudero et al. (2008).

Usage

data(M5data)

Format

The first two columns are the two variables. The last column is the true classification vector where symbol "0" stands for the contaminating data points.

Source

García-Escudero, L.A.; Gordaliza, A.; Matrán, C. and Mayo-Iscar, A. (2008), "A General Trimming Approach to Robust Cluster Analysis". Annals of Statistics, Vol.36, pp. 1324-1345.

Examples

#--- EXAMPLE 1 ------------------------------------------ 
data (M5data) 
x <- M5data[, 1:2] 
clus <- tclust(x, k=3, alpha=0.1, nstart=200, niter1=3, niter2=17, 
   nkeep=10, opt="HARD", equal.weights=FALSE, restr.fact=50, trace=TRUE) 
plot (x, col=clus$cluster+1)

Classification Trimmed Likelihood Curves

Description

The function applies tclust several times on a given dataset while parameters alpha and k are altered. The resulting object gives an idea of the optimal trimming level and number of clusters considering a particular dataset.

Usage

ctlcurves(
  x,
  k = 1:4,
  alpha = seq(0, 0.2, len = 6),
  restr.fact = 50,
  parallel = FALSE,
  trace = 1,
  ...
)

Arguments

Details

These curves show the values of the trimmed classification (log-)likelihoods when altering the trimming proportion alpha and the number of clusters k. The careful examination of these curves provides valuable information for choosing these parameters in a clustering problem. For instance, an appropriate k to be chosen is one that we do not observe a clear increase in the trimmed classification likelihood curve for k with respect to the k+1 curve for almost all the range of alpha values. Moreover, an appropriate choice of parameter alpha may be derived by determining where an initial fast increase of the trimmed classification likelihood curve stops for the final chosen k. A more detailed explanation can be found in García-Escudero et al. (2011).

Value

The function returns an S3 object of type ctlcurves containing the following components:

• par A list containing all the parameters passed to this function

• obj An array containing the objective functions values of each computed cluster-solution

• min.weights An array containing the minimum cluster weight of each computed cluster-solution

References

García-Escudero, L.A.; Gordaliza, A.; Matrán, C. and Mayo-Iscar, A. (2011), "Exploring the number of groups in robust model-based clustering." Statistics and Computing, 21 pp. 585-599, <doi:10.1007/s11222-010-9194-z>

Examples

 #--- EXAMPLE 1 ------------------------------------------

 sig <- diag (2)
 cen <- rep (1, 2)
 x <- rbind(MASS::mvrnorm(108, cen * 0,   sig),
 	       MASS::mvrnorm(162, cen * 5,   sig * 6 - 2),
 	       MASS::mvrnorm(30, cen * 2.5, sig * 50))

 ctl <- ctlcurves(x, k = 1:4)
 ctl

   ##  ctl-curves 
 plot(ctl)  ##  --> selecting k = 2, alpha = 0.08

   ##  the selected model 
 plot(tclust(x, k = 2, alpha = 0.08, restr.fact = 7))

 #--- EXAMPLE 2 ------------------------------------------

 data(geyser2)
 ctl <- ctlcurves(geyser2, k = 1:5)
 ctl
 
   ##  ctl-curves 
 plot(ctl)  ##  --> selecting k = 3, alpha = 0.08

   ##  the selected model
 plot(tclust(geyser2, k = 3, alpha = 0.08, restr.fact = 5))


 #--- EXAMPLE 3 ------------------------------------------
 
 data(swissbank)
 ctl <- ctlcurves(swissbank, k = 1:5, alpha = seq (0, 0.3, by = 0.025))
 ctl
 
   ##  ctl-curves 
 plot(ctl)  ##  --> selecting k = 2, alpha = 0.1
 
   ##  the selected model
 plot(tclust(swissbank, k = 2, alpha = 0.1, restr.fact = 50))
 

Function to perform the E-step for a Gaussian mixture distribution

Description

Compute the log PDF for each observation, the posterior probabilities and the objective function (total log-likelihood) for a Gaussian mixture distribution

Arguments

Details

Formally a mixture model corresponds to the mixture distribution that represents the probability distribution of observations in the overall population. Mixture models are used to make statistical inferences about the properties of the sub-populations given only observations on the pooled population, without sub-population-identity information. Mixture modeling approaches assume that data at hand $y_1, ..., y_n in R^p come from a probability distribution with density given by the sum of k components

\sum_{j=1}^k \pi_j \phi( \cdot, \theta_j)

with \phi( \cdot, \theta_j) being the p-variate (generally multivariate normal) densities with parameters \theta_j, j=1, \ldots, k. Generally \theta_j= (\mu_j, \Sigma_j) where \mu_j is the population mean and \Sigma_j is the covariance matrix for component j. \pi_j is the (prior) probability of component j. The objective function is obj is equal to

obj = \log \left( \prod_{i=1}^n \sum_{j=1}^k \pi_j \phi (y_i; \; \theta_j) \right)

or

obj = \sum_{i=1}^n \log \left( \sum_{j=1}^k \pi_j \phi (y_i; \; \theta_j) \right)

where k is the number of components of the mixture and \pi_j are the component probabilitites and \theta_j are the parameters of the j-th mixture component.

Value

The function returns a list with the following elements:

• obj The value of the objective function (total log-likelihood)

• postprob an n-by-k matrix with the posterior probablilities

• logpdf a vector of length n containing the log PDF for each observation

References

McLachlan, G.J.; Peel, D. (2000). Finite Mixture Models. Wiley. ISBN 0-471-00626-2

Examples

##      Generate two Gaussian normal distributions
##      and do not produce plots

       mu1 = c(1,2)
       sigma1 = matrix(c(2, 0, 0, .5), nrow=2, byrow=TRUE)    #[2 0; 0 .5];
       mu2 = c(-3, -5)
       sigma2 = matrix(c(1, 0, 0, 1), nrow=2, byrow=TRUE)
       n1 = 100
       n2 = 200
       Y = rbind(MASS::mvrnorm(n1, mu1, sigma1), 
                 MASS::mvrnorm(n2, mu2, sigma2))
       k = 2
       pi = c(1/3, 2/3)
       mu = rbind(mu1, mu2)
       sigma = array(0, dim=c(2,2,2))
       sigma[,,1] = sigma1
       sigma[,,2] = sigma2
       
       ll = matrix(0, nrow=n1+n2, ncol=2)
       for(j in 1:k)
           ll[,j] = log(pi[j]) +  tclust:::dmvnrm(Y, mu[j,], sigma[,,j])

       dd = tclust:::estepRR(ll)
       dd$obj
       dd$logpdf
       dd$postprob

Flea

Description

Flea-beetle measurements

Usage

data(flea)

Format

A data frame with 74 rows and 7 variables: six explanatory and one response variable - species. The variables are as follows:

• tars1: width of the first joint of the first tarsus in microns (the sum of measurements for both tarsi)

• tars2: the same for the second joint

• head: the maximal width of the head between the external edges of the eyes in 0.01 mm

• ade1: the maximal width of the aedeagus in the fore-part in microns

• ade2: the front angle of the aedeagus ( 1 unit = 7.5 degrees)

• ade3: the aedeagus width from the side in microns

• species, which species is being examined - Concinna, Heptapotamica, Heikertingeri

References

A. A. Lubischew (1962), On the Use of Discriminant Functions in Taxonomy, Biometrics, 184 pp.455–477.

Examples

 data(flea)
 head(flea)

Old Faithful Geyser Data

Description

A bivariate data set obtained from the Old Faithful Geyser, containing the eruption length and the length of the previous eruption for 271 eruptions of this geyser in minutes.

Usage

data(geyser2)

Format

A data frame containing 272 observations in 2 variables. The variables are as follows:

• Eruption length The eruption length in minutes.

• Previous eruption length The length of the previous eruption in minutes.

Source

This particular data structure can be obtained by applying the following code to the "Old Faithful Geyser" (faithful data set (Härdle 1991) in the package datasets):
f1 <- faithful[,1]
geyser2 <- cbind(f1[-length(f1)], f1[-1])
colnames(geyser2) <- c("Eruption length",
"Previous eruption length")

References

García-Escudero, L.A. and Gordaliza, A. (1999). Robustness properties of k-means and trimmed k-means. Journal of the American Statistical Assoc., Vol.94, No.447, 956–969.

Härdle, W. (1991). Smoothing Techniques with Implementation in S., New York: Springer.

mixsym

Description

A simulated bivariate data set of size n = 100 and dimension p = 2 from a k = 3 component mixture obtained by applying the MixSim method of Maitra and Melnykov (2010), as extended by Riani et al. (2015) and incorporated into the FSDA toolbox of Matlab (Riani et al., 2012). The data set has been generated by imposing an average cluster overlap (defined as a sum of pairwise misclassification probabilities) equal to 0.04 and a maximum eigenvalue ratio for the scatters matrices equal to 5.

Usage

data(mixsym)

Format

A data frame with 10 rows and 3 variables: 2 numerical and one categorical - the thrue cluster. The variables are as follows:

• X1: first numerical variable

• X2: second numerical variable

• class: the true cluster

Details

Simulated bivariate data set

References

Maitra, R. and Melnykov, V. (2010). Simulating data to study performance of finite mixture modeling and clustering algorithms. J. Comput. Graph. Stat., 19:354– 376.

Riani, M., Cerioli, A., Perrotta, D., and Torti, F. (2015). Simulating mixtures of multivariate data with fixed cluster overlap in FSDA library. Adv. Data Anal. Classif., 9:2015.

Riani, M., Perrotta, D., and Torti, F. (2012). FSDA: a matlab toolbox for robust analysis and interactive data exploration,. Chemometr. Intell. Lab. Syst., 116:17–32.

Examples

 data(mixsym)
 head(mixsym)

Pinus nigra dataset

Description

To study the growth of the wood mass in a cultivated forest of Pinus nigra located in the north of Palencia (Spain), a sample of 362 trees was studied. The data set is made of measurements of heights (in meters), in variable "HT", and diameters (in millimetres), in variable "Diameter", of these trees. The presence of three linear groups can be guessed apart from a small group of trees forming its own cluster with larger heights and diameters one isolated tree with the largest diameter but small height. More details on the interpretation of this dataset in García-Escudero et al (2010).

Usage

data(pine)

Format

A data frame containing 362 observations in 2 variables. The variables are as follows:

• Diameter Diameter

• HT Height

References

García-Escudero, L. A., Gordaliza, A., Mayo-Iscar, A., and San Martín, R. (2010). Robust clusterwise linear regression through trimming. Computational Statistics & Data Analysis, 54(12), 3057–3069.

The plot method for objects of class DiscrFact

Description

The plot method for class DiscrFact: Next to a plot of the tclust object which has been used for creating the DiscrFact object, a silhouette plot indicates the presence of groups with a large amount of doubtfully assigned observations. A third plot similar to the standard tclust plot serves to highlight the identified doubtful observations.

Usage

## S3 method for class 'DiscrFact'
plot(
  x,
  enum.plots = FALSE,
  xlab = "Discriminant Factor",
  ylab = "Clusters",
  print.DiscrFact = TRUE,
  xlim,
  col.nodoubt = grey(0.8),
  by.cluster = FALSE,
  ...
)

Arguments

Details

plot_DiscrFact_p2 displays a silhouette plot based on the discriminant factors of the observations. A solution with many large discriminant factors is not reliable. Such clusters can be identified with this silhouette plot. Thus plot_DiscrFact_p3 displays the dataset, highlighting observations with discriminant factors greater than the given threshold. The function plot.DiscrFact() combines the standard plot of a tclust object, and the two plots introduced here.

References

García-Escudero, L.A.; Gordaliza, A.; Matrán, C. and Mayo-Iscar, A. (2011), "Exploring the number of groups in robust model-based clustering." Statistics and Computing, 21 pp. 585-599, <doi:10.1007/s11222-010-9194-z>

Examples

 sig <- diag (2)
 cen <- rep (1, 2)
 x <- rbind(MASS::mvrnorm(360, cen * 0,   sig),
 	       MASS::mvrnorm(540, cen * 5,   sig * 6 - 2),
 	       MASS::mvrnorm(100, cen * 2.5, sig * 50))

 clus.1 <- tclust(x, k = 2, alpha=0.1, restr.fact=12)
 clus.2 <- tclust(x, k = 3, alpha=0.1, restr.fact=1)

 dsc.1 <- DiscrFact(clus.1)
 plot(dsc.1)

 dsc.2 <- DiscrFact(clus.2)
 plot(dsc.2)

The plot method for objects of class ctlcurves

Description

The plot method for class ctlcurves: This function implements a series of plots, which display characteristic values of the each model, computed with different values for k and alpha.

Usage

## S3 method for class 'ctlcurves'
plot(
  x,
  what = c("obj", "min.weights", "doubtful"),
  main,
  xlab,
  ylab,
  xlim,
  ylim,
  col,
  lty = 1,
  ...
)

Arguments

Details

These curves show the values of the trimmed classification (log-)likelihoods when altering the trimming proportion alpha and the number of clusters k. The careful examination of these curves provides valuable information for choosing these parameters in a clustering problem. For instance, an appropriate k to be chosen is one that we do not observe a clear increase in the trimmed classification likelihood curve for k with respect to the k+1 curve for almost all the range of alpha values. Moreover, an appropriate choice of parameter alpha may be derived by determining where an initial fast increase of the trimmed classification likelihood curve stops for the final chosen k. A more detailed explanation can be found in García-Escudero et al. (2011).

This function implements a series of plots, which display characteristic values of the each model, computed with different values for k and alpha.

"obj"

Objective function values.

"min.weights"

The minimum cluster weight found for each computed model. This plot is intended to spot spurious clusters, which in general yield quite small weights.

"doubtful"

The number of "doubtful" decisions identified by DiscrFact.

References

García-Escudero, L.A.; Gordaliza, A.; Matrán, C. and Mayo-Iscar, A. (2011), "Exploring the number of groups in robust model-based clustering." Statistics and Computing, 21 pp. 585-599, <doi:10.1007/s11222-010-9194-z>

Examples

 #--- EXAMPLE 1 ------------------------------------------

 
 sig <- diag (2)
 cen <- rep (1, 2)
 x <- rbind(MASS::mvrnorm(108, cen * 0,   sig),
 	       MASS::mvrnorm(162, cen * 5,   sig * 6 - 2),
 	       MASS::mvrnorm(30, cen * 2.5, sig * 50))

 (ctl <- ctlcurves(x, k = 1:4))

 plot(ctl)
 

Plot an 'rlg' object

Description

Different plots for the results of 'rlg' analysis, stored in an rlg object, see Details.

Usage

## S3 method for class 'rlg'
plot(
  x,
  which = c("all", "scores", "loadings", "eigenvalues"),
  sort = TRUE,
  ask = (which == "all" && dev.interactive(TRUE)),
  ...
)

Arguments

Examples

 data (LG5data)
 x <- LG5data[, 1:10]
 clus <- rlg(x, d = c(2,2,2), alpha=0.1)
 plot(clus, which="eigenvalues") 
 plot(clus, which="scores") 

Plot Method for tclust and tkmeans Objects

Description

One and two dimensional structures are treated separately (e.g. tolerance intervals/ellipses are displayed). Higher dimensional structures are displayed by plotting the two first Fisher's canonical coordinates (evaluated by tclust::discr_coords) and derived from the final cluster assignments (trimmed observations are not taken into account). plot.tclust.Nd can be called with one or two-dimensional tclust- or tkmeans-objects too. The function fails, if store.x = FALSE is specified in the tclust() or tkmeans() call, because the original data matrix is required here.

Usage

## S3 method for class 'tclust'
plot(x, ...)

## S3 method for class 'tkmeans'
plot(x, ...)

Arguments

Details

The plot method for classes tclust and tkmeans.

Further Arguments

• xlab, ylab, xlim, ylim, pch, col Arguments passed to plot().

• main The title of the plot. Use "/p" for displaying the chosen parameters alpha and k or "/r" for plotting the chosen restriction.

• main.pre An optional string which is added to the plot's caption.

• sub A string specifying the subtitle of the plot. Use "/p" (default) for displaying the chosen parameters alpha and k, "/r" for plotting the chosen restriction and "/pr" for both.

• sub1 A secondary (optional) subtitle.

• labels A string specifying the type of labels to be drawn. Either labels="none" (default), labels="cluster" or labels="observation" can be specified. If specified, parameter pch is ignored.

• text A vector of length n (the number of observations) containing strings which are used as labels for each observation. If specified, the parameters labels and pch are ignored.

• by.cluster Logical value indicating whether parameters pch and col refer to observations (FALSE) or clusters (TRUE).

• jitter.y Logical value, specifying whether the drawn values shall be jittered in y-direction for better visibility of structures in 1 dimensional data.

• tol The tolerance interval. 95% tolerance ellipsoids (assuming normality) are plotted by default.

• tol.col, tol.lty, tol.lwd Vectors of length k or 1 containing the col, lty and lwd arguments for the tolerance ellipses/lines.

Examples

 #--- EXAMPLE 1------------------------------
 sig <- diag (2)
 cen <- rep (1, 2)
 x <- rbind(MASS::mvrnorm(360, cen * 0,   sig),
 	       MASS::mvrnorm(540, cen * 5,   sig * 6 - 2),
 	       MASS::mvrnorm(100, cen * 2.5, sig * 50))
 # Two groups and 10\% trimming level
 a <- tclust(x, k = 2, alpha = 0.1, restr.fact = 12)
 plot (a)
 plot (a, labels = "observation")
 plot (a, labels = "cluster")
 plot (a, by.cluster = TRUE)
 #--- EXAMPLE 2------------------------------
 sig <- diag (2)
 cen <- rep (1, 2)
 x <- rbind(MASS::mvrnorm(360, cen * 0,   sig),
 	       MASS::mvrnorm(540, cen * 5,   sig),
 	       MASS::mvrnorm(100, cen * 2.5, sig))
 # Two groups and 10\% trimming level
 a <- tkmeans(x, k = 2, alpha = 0.1)
 plot (a)
 plot (a, labels = "observation")
 plot (a, labels = "cluster")
 plot (a, by.cluster = TRUE)

The plot method for objects of class tclustIC

Description

The plot method for class tclustIC: This function implements a series of plots, which display characteristic values of each model, computed with different values for k and c for a fixed alpha.

Usage

## S3 method for class 'tclustIC'
plot(x, whichIC, cc, main, xlab, ylab, xlim, ylim, col, lty, ...)

Arguments

References

Cerioli, A., Garcia-Escudero, L.A., Mayo-Iscar, A. and Riani M. (2017). Finding the Number of Groups in Model-Based Clustering via Constrained Likelihoods, Journal of Computational and Graphical Statistics, pp. 404-416, https://doi.org/10.1080/10618600.2017.1390469.

Examples

 
 sig <- diag (2)
 cen <- rep (1, 2)
 x <- rbind(MASS::mvrnorm(108, cen * 0,   sig),
 	       MASS::mvrnorm(162, cen * 5,   sig * 6 - 2),
 	       MASS::mvrnorm(30, cen * 2.5, sig * 50))

 (out <- tclustIC(x, whichIC="ALL"))

 plot(out)
 

Plot Method for tclustICsol Objects

Description

Displays one of the solutions, selected by the argument 'sol'. The default display is a scatterplot matrix of the data using colors and symbols of the observations to identify the groups in the selected solution. If the argument 'choice' is specified and its length is two, a simple scatterplot will be shown. The function fails, if store.x = FALSE is specified in the tclustICsol() call because the original data matrix is required here.

Usage

## S3 method for class 'tclustICsol'
plot(x, whichIC, sol = 1, col, pch, main, sub1, choice, ...)

Arguments

Details

The plot method for class tclustICsol.

Examples

 #--- EXAMPLE 1 ------------------------------------------
 
 data(geyser2)
 (out <- tclustIC(geyser2, whichIC="MIXMIX", alpha=0.1))

 ##  Show the first two best solutions using as Information criterion MIXMIX
 cat("\nBest solutions using MIXMIX\n")
 outsol <- tclust::tclustICsol(out, whichIC="MIXMIX", nsol=2)
 plot(outsol)
 plot(outsol, choice=c(1,2))
 plot(outsol, choice=c(1,2), xlab="XLAB", ylab="YLAB")
 

Calculates Rand type Indices to compare two partitions

Description

Calculates Rand type Indices to compare two partitions

Usage

randIndex(c1, c2 = NULL, noisecluster = NULL)

Arguments

Value

A list with Rand type indexes:

• AR Adjusted Rand index. A number between -1 and 1. The adjusted Rand index is the corrected-for-chance version of the Rand index.

• RI Rand index (unadjusted). A number between 0 and 1. Rand index computes the fraction of pairs of objects for which both classification methods agree. RI ranges from 0 (no pair classified in the same way under both clusterings) to 1 (identical clusterings).

• MI Mirkin's index. A number between 0 and 1. Mirkin's index computes the percentage of pairs of objects for which both classification methods disagree. MI=1-RI.

• HI Hubert index. A number between -1 and 1. HI index is equal to the fraction of pairs of objects for which both classification methods agree minus the fraction of pairs of objects for which both classification methods disagree. HI= RI-MI.

Examples

##  1. randindex with the contingency table as input.
T <- matrix(c(1, 1, 0, 1, 2, 1, 0, 0, 4), nrow=3)
(ARI <- randIndex(T))

##  2. randindex with the two vectors as input.
c <- matrix(c(1, 1, 1, 2, 2, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3), ncol=2, byrow=TRUE)
## c1 = numeric vector containing the labels of the first partition
c1 <- c[,1]
## c2 = numeric vector containing the labels of the second partition
c2 <- c[,2]

(ARI <- randIndex(c1,c2))

##  3. Compare ARI for iris data (true classification against tclust classification)
library(tclust)
c1 <- iris$Species  # first partition c1 is the true partition
out <- tclust(iris[, 1:4], k=3, alpha=0, restr.fact=100)
c2 <- out$cluster   # second partition c2 is the output of tclust clustering procedure

randIndex(c1,c2)

##  4. Compare ARI for iris data (exclude unassigned units from tclust).

c1 <- iris$Species      # first partition c1 is the true partition
out <- tclust(iris[,1:4], k=3, alpha=0.1, restr.fact=100)
c2 <- out$cluster       #  second partition c2 is the output of tclust clustering procedure

## Units inside c2 which contain number 0 are referred to trimmed observations
noisecluster <- 0
randIndex(c1, c2, noisecluster=0)

Robust Linear Grouping

Description

The function rlg() searches for clusters around affine subspaces of dimensions given by vector d (the length of that vector is the number of clusters). For instance d=c(1,2) means that we are clustering around a line and a plane. For robustifying the estimation, a proportion alpha of observations is trimmed. In particular, the trimmed k-means method is represented by the rlg method, if d=c(0,0,..0) (a vector of length k with zeroes).

Usage

rlg(
  x,
  d,
  alpha = 0.05,
  nstart = 500,
  niter1 = 3,
  niter2 = 20,
  nkeep = 5,
  scale = FALSE,
  parallel = FALSE,
  n.cores = -1,
  trace = FALSE
)

Arguments

Details

The procedure allows to deal with robust clustering around affine subspaces with an alpha proportion of trimming level by minimizing the trimmed sums of squared orthogonal residuals. Each component of vector d indicates the intrinsic dimension of the affine subspace where observations on that cluster are going to be clustered. Therefore a component equal to 0 on that vector implies clustering around centres, equal to 1 around lines, equal to 2 around planes and so on. The procedure so allows simultaneous clustering and dimensionality reduction.

This iterative algorithm performs "concentration steps" to improve the current cluster assignments. For approximately obtaining the global optimum, the procedure is randomly initialized nstart times and niter1 concentration steps are performed for them. The nkeep most “promising” iterations, i.e. the nkeep iterated solutions with the initial best values for the target function, are then iterated until convergence or until niter2 concentration steps are done.

Value

Returns an object of class rlg which is basically a list with the following elements:

• centers - A matrix of size p x k containing the location vectors (column-wise) of each cluster.

• U - A list with k elements where each element is p x d_j matrix whose d_j columns are unitary and orthogonal vectors generating the affine subspace (after subtracting the corresponding cluster’s location parameter in centers). d_j is the intrinsic dimension of the affine subspace approximation in the j-th cluster, i.e., the elements of vector d.

• cluster - A numerical vector of size n containing the cluster assignment for each observation. Cluster names are integer numbers from 1 to k, 0 indicates trimmed observations.

• obj - The value of the objective function of the best (returned) solution.

• cluster.ini - A matrix with nstart rows and number of columns equal to the number of observations and where each row shows the final clustering assignments (0 for trimmed observations) obtained after the niter1 iteration of the nstart random initializations.

• obj.ini -A numerical vector of length nstart containing the values of the target function obtained after the niter1 iteration of the nstart random initializations.

• x - The input data set.

• dimensions - The input d vector with the intrinsic dimensions. The number of clusters is the length of that vector.

• alpha - The input trimming level.

Author(s)

Javier Crespo Guerrero, Jesús Fernández Iglesias, Luis Angel Garcia Escudero, Agustin Mayo Iscar.

References

García‐Escudero, L. A., Gordaliza, A., San Martin, R., Van Aelst, S., & Zamar, R. (2009). Robust linear clustering. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 71, 301-318.

Examples

##--- EXAMPLE 1 ------------------------------------------
data (LG5data)
x <- LG5data[, 1:10]
clus <- rlg(x, d = c(2,2,2), alpha=0.1)
plot(x, col=clus$cluster+1)
plot(clus, which="eigenvalues") 
plot(clus, which="scores") 

##--- EXAMPLE 2 ------------------------------------------
 data (pine) 
 clus <- rlg(pine, d = c(1,1,1), alpha=0.035)
 plot(pine, col=clus$cluster+1)
 

Simulate contaminated data set for applying rlg

Description

Simulate alpha*100% contaminated data set for applying rlg by generating a k=3 components with equal size and # common underlying dimension q_1=q_2=q_3=q

Usage

simula.rlg(q = 2, p = 10, n = 200, var = 0.01, sep.means = 0, alpha = 0.05)

Arguments

Value

a list with the following items

• x - The generated dataset

• true - The true classification

Examples

 res <- simula.rlg(q=5, p=200, n=150, var=0.01, sep.means=0.00)
 plot(res$x,col=res$true+1)

Simulate contaminated data set for applying TCLUST

Description

Simulate 10% contaminated data set for applying TCLUST

Usage

simula.tclust(n, p = 4, k = 3, type = 2, balanced = 1)

Arguments

Value

a list with the following items

• x - The generated dataset

• true - The true classification

Examples

res <- simula.tclust(n=400,k=3,p=8,type=2,balanced=1)
plot(res$x,col=res$true+1)

The summary method for objects of class DiscrFact

Description

The summary method for class DiscrFact.

Usage

## S3 method for class 'DiscrFact'
summary(object, hide.emtpy = TRUE, show.clust, show.alt, ...)

Arguments

References

García-Escudero, L.A.; Gordaliza, A.; Matrán, C. and Mayo-Iscar, A. (2011), "Exploring the number of groups in robust model-based clustering." Statistics and Computing, 21 pp. 585-599, <doi:10.1007/s11222-010-9194-z>

Examples

 sig <- diag (2)
 cen <- rep (1, 2)
 x <- rbind(MASS::mvrnorm(360, cen * 0,   sig),
 	       MASS::mvrnorm(540, cen * 5,   sig * 6 - 2),
 	       MASS::mvrnorm(100, cen * 2.5, sig * 50)
 )

 clus.1 <- tclust(x, k = 2, alpha=0.1, restr.fact=12)
 clus.2 <- tclust(x, k = 3, alpha=0.1, restr.fact=1)

 dsc.1 <- DiscrFact(clus.1)
 summary(dsc.1)

 dsc.2 <- DiscrFact(clus.2)
 summary(dsc.2)

Swiss banknotes data

Description

Six variables measured on 100 genuine and 100 counterfeit old Swiss 1000-franc bank notes (Flury and Riedwyl, 1988).

Usage

data(swissbank)

Format

A data frame containing 200 observations in 6 variables. The variables are as follows:

• Length Length of the bank note

• Ht_Left Height of the bank note, measured on the left

• Ht_Right Height of the bank note, measured on the right

• IF_Lower Distance of inner frame to the lower border

• IF_Upper Distance of inner frame to the upper border

• Diagonal Length of the diagonal

Details

Observations 1–100 are the genuine bank notes and the other 100 observations are the counterfeit bank notes.

Source

Flury, B. and Riedwyl, H. (1988). Multivariate Statistics, A Practical Approach, Cambridge University Press.

TCLUST method for robust clustering

Description

This function searches for k (or less) clusters with different covariance structures in a data matrix x. Relative cluster scatter can be restricted when restr="eigen" by constraining the ratio between the largest and the smallest of the scatter matrices eigenvalues by a constant value restr.fact. Relative cluster scatters can be also restricted with restr="deter" by constraining the ratio between the largest and the smallest of the scatter matrices' determinants.

For robustifying the estimation, a proportion alpha of observations is trimmed. In particular, the trimmed k-means method is represented by the tclust() method, by setting parameters restr.fact=1, opt="HARD" and equal.weights=TRUE.

Usage

tclust(
  x,
  k,
  alpha = 0.05,
  nstart = 500,
  niter1 = 3,
  niter2 = 20,
  nkeep = 5,
  iter.max,
  equal.weights = FALSE,
  restr = c("eigen", "deter"),
  restr.fact = 12,
  cshape = 1e+10,
  opt = c("HARD", "MIXT"),
  center = FALSE,
  scale = FALSE,
  store_x = TRUE,
  parallel = FALSE,
  n.cores = -1,
  zero_tol = 1e-16,
  drop.empty.clust = TRUE,
  trace = 0
)

Arguments

Details

The procedure allows to deal with robust clustering with an alpha proportion of trimming level and searching for k clusters. We are considering classification trimmed likelihood when using opt=”HARD” so that “hard” or “crisp” clustering assignments are done. On the other hand, mixture trimmed likelihood are applied when using opt=”MIXT” so providing a kind of clusters “posterior” probabilities for the observations. Relative cluster scatter can be restricted when restr="eigen" by constraining the ratio between the largest and the smallest of the scatter matrices eigenvalues by a constant value restr.fact. Setting restr.fact=1, yields the strongest restriction, forcing all clusters to be spherical and equally scattered. Relative cluster scatters can be also restricted with restr="deter" by constraining the ratio between the largest and the smallest of the scatter matrices' determinants.

This iterative algorithm performs "concentration steps" to improve the current cluster assignments. For approximately obtaining the global optimum, the procedure is randomly initialized nstart times and niter1 concentration steps are performed for them. The nkeep most “promising” iterations, i.e. the nkeep iterated solutions with the initial best values for the target function, are then iterated until convergence or until niter2 concentration steps are done.

The parameter restr.fact defines the cluster scatter matrices restrictions, which are applied on all clusters during each concentration step. It restricts the ratio between the maximum and minimum eigenvalue of all clusters' covariance structures to that parameter. Setting restr.fact=1, yields the strongest restriction, forcing all clusters to be spherical and equally scattered.

Cluster components with similar sizes are favoured when considering equal.weights=TRUE while equal.weights=FALSE admits possible different prior probabilities for the components and it can easily return empty clusters when the number of clusters is greater than apparently needed.

Value

The function returns the following values:

• cluster - A numerical vector of size n containing the cluster assignment for each observation. Cluster names are integer numbers from 1 to k, 0 indicates trimmed observations. Note that it could be empty clusters with no observations when equal.weights=FALSE.

• obj - The value of the objective function of the best (returned) solution.

• NlogL - A value related to the classification log-likelihood of the best (returned) solution. If opt=="HARD", NlogL = -2*obj.

• size - An integer vector of size k, returning the number of observations contained by each cluster.

• weights - Vector of Cluster weights

• centers - A matrix of size p x k containing the centers (column-wise) of each cluster.

• cov - An array of size p x p x k containing the covariance matrices of each cluster.

• code - A numerical value indicating if the concentration steps have converged for the returned solution (2).

• posterior - A matrix with k columns that contains the posterior probabilities of membership of each observation (row-wise) to the k clusters. This posterior probabilities are 0-1 values in the opt="HARD" case. Trimmed observations have 0 membership probabilities to all clusters.

• MIXMIX - BIC which based on the parameters estimated through the mixture log-likelihood and the maximized mixture likelihood as goodness of fit measure. This output is present only if opt="MIXT".

• MIXMIX - BIC which uses the classification likelihood based on parameters estimated through the mixture likelihood (In some books this quantity is called ICL). This output is present only if opt="MIXT".

• CLACLA - BIC which uses the classification likelihood based on parameters estimated using the classification likelihood. This output is present only if opt="HARD".

• cluster.ini - A matrix with nstart rows and number of columns equal to the number of observations and where each row shows the final clustering assignments (0 for trimmed observations) obtained after the niter1 iteration of the nstart random initializations.

• obj.ini - A numerical vector of length nstart containing the values of the target function obtained after the niter1 iteration of the nstart random initializations.

• x - The input data set.

• k - The input number of clusters.

• alpha - The input trimming level.

Author(s)

Javier Crespo Guerrero, Luis Angel Garcia Escudero, Agustin Mayo Iscar.

References

Fritz, H.; Garcia-Escudero, L.A.; Mayo-Iscar, A. (2012), "tclust: An R Package for a Trimming Approach to Cluster Analysis". Journal of Statistical Software, 47(12), 1-26. URL http://www.jstatsoft.org/v47/i12/

Garcia-Escudero, L.A.; Gordaliza, A.; Matran, C. and Mayo-Iscar, A. (2008), "A General Trimming Approach to Robust Cluster Analysis". Annals of Statistics, Vol.36, 1324–1345.

García-Escudero, L. A., Gordaliza, A. and Mayo-Íscar, A. (2014). A constrained robust proposal for mixture modeling avoiding spurious solutions. Advances in Data Analysis and Classification, 27–43.

García-Escudero, L. A., and Mayo-Íscar, A. and Riani, M. (2020). Model-based clustering with determinant-and-shape constraint. Statistics and Computing, 30, 1363–1380.]

Examples

 
 ##--- EXAMPLE 1 ------------------------------------------
 sig <- diag(2)
 cen <- rep(1,2)
 x <- rbind(MASS::mvrnorm(360, cen * 0,   sig),
            MASS::mvrnorm(540, cen * 5,   sig * 6 - 2),
            MASS::mvrnorm(100, cen * 2.5, sig * 50))
 
 ## Two groups and 10\% trimming level
 clus <- tclust(x, k = 2, alpha = 0.1, restr.fact = 8)
 
 plot(clus)
 plot(clus, labels = "observation")
 plot(clus, labels = "cluster")
 
 ## Three groups (one of them very scattered) and 0\% trimming level
 clus <- tclust(x, k = 3, alpha=0.0, restr.fact = 100)
 
 plot(clus)
 
 ##--- EXAMPLE 2 ------------------------------------------
 data(geyser2)
 (clus <- tclust(geyser2, k = 3, alpha = 0.03))
 
 plot(clus)
 


 ##--- EXAMPLE 3 ------------------------------------------
 data(M5data)
 x <- M5data[, 1:2]
 
 clus.a <- tclust(x, k = 3, alpha = 0.1, restr.fact =  1,
                   restr = "eigen", equal.weights = TRUE)
 clus.b <- tclust(x, k = 3, alpha = 0.1, restr.fact =  50,
                    restr = "eigen", equal.weights = FALSE)
 clus.c <- tclust(x, k = 3, alpha = 0.1, restr.fact =  1,
                   restr = "deter", equal.weights = TRUE)
 clus.d <- tclust(x, k = 3, alpha = 0.1, restr.fact = 50,
                   restr = "deter", equal.weights = FALSE)
 
 pa <- par(mfrow = c (2, 2))
 plot(clus.a, main = "(a)")
 plot(clus.b, main = "(b)")
 plot(clus.c, main = "(c)")
 plot(clus.d, main = "(d)")
 par(pa)
 
 ##--- EXAMPLE 4 ------------------------------------------

 data (swissbank)
 ## Two clusters and 8\% trimming level
 (clus <- tclust(swissbank, k = 2, alpha = 0.08, restr.fact = 50))
 
 ## Pairs plot of the clustering solution
 pairs(swissbank, col = clus$cluster + 1)
 ## Two coordinates
 plot(swissbank[, 4], swissbank[, 6], col = clus$cluster + 1,
      xlab = "Distance of the inner frame to lower border",
      ylab = "Length of the diagonal")
 plot(clus)
 
 ## Three clusters and 0\% trimming level
 clus<- tclust(swissbank, k = 3, alpha = 0.0, restr.fact = 110)
 
 ## Pairs plot of the clustering solution
 pairs(swissbank, col = clus$cluster + 1)
 
 ## Two coordinates
 plot(swissbank[, 4], swissbank[, 6], col = clus$cluster + 1, 
       xlab = "Distance of the inner frame to lower border", 
       ylab = "Length of the diagonal")
 
 plot(clus)
 
 ##--- EXAMPLE 5 ------------------------------------------
  data(M5data)
  x <- M5data[, 1:2]
  
  ## Classification trimmed likelihood approach
  clus.a <- tclust(x, k = 3, alpha = 0.1, restr.fact =  50,
                     opt="HARD", restr = "eigen", equal.weights = FALSE)
 ## Mixture trimmed likelihood approach
  clus.b <- tclust(x, k = 3, alpha = 0.1, restr.fact =  50,
                     opt="MIXT", restr = "eigen", equal.weights = FALSE)
 
 ## Hard 0-1 cluster assignment (all 0 if trimmed unit)
 head(clus.a$posterior)
 
 ## Posterior probabilities cluster assignment for the
 ##  mixture approach (all 0 if trimmed unit)
 head(clus.b$posterior)
 

Performs cluster analysis by calling tclust for different number of groups k and restriction factors c

Description

Computes the values of BIC (MIXMIX), ICL (MIXCLA) or CLA (CLACLA), for different values of k (number of groups) and different values of c (restriction factor), for a prespecified level of trimming (the last two letters in the name stand for 'Information Criterion').

Usage

tclustIC(
  x,
  kk = 1:5,
  cc = c(1, 2, 4, 8, 16, 32, 64, 128),
  alpha = 0.05,
  whichIC = c("ALL", "MIXMIX", "MIXCLA", "CLACLA"),
  parallel = FALSE,
  n.cores = -1,
  trace = FALSE,
  ...
)

Arguments

Value

The functions print() and summary() are used to obtain and print a summary of the results. The function returns an S3 object of type tclustIC containing the following components:

• call the matched call

• kk a vector containing the values of k (number of components) which have been considered. This vector is identical to the optional argument kk (default is kk=1:5.

• cc a vector containing the values of c (values of the restriction factor) which have been considered. This vector is identical to the optional argument cc (defalt is cc=c(1, 2, 4, 8, 16, 32, 64, 128).

• alpha trimming level

• whichIC Information criteria used

• CLACLA a matrix of size length(kk)-times-length(cc) containinig the value of the penalized classification likelihood. This output is present only if whichIC="CLACLA" or whichIC="ALL".

• IDXCLA a matrix of lists of size length(kk)-times-length(cc) containinig the assignment of each unit using the classification model. This output is present only if whichIC="CLACLA" or whichIC="ALL".

• MIXMIX a matrix of size length(kk)-times-length(cc) containinig the value of the penalized mixtrue likelihood. This output is present only if whichIC="MIXMIX" or whichIC="ALL".

• IDXMIX a matrix of lists of size length(kk)-times-length(cc) containinig the assignment of each unit using the classification model. This output is present only if whichIC="MIXMIX" or whichIC="ALL".

• MIXCLA a matrix of size length(kk)-times-length(cc) containinig the value of the ICL criterion. This output is present only if whichIC="MIXCLA" or whichIC="ALL".

• x the input data matrix of size length(n)-times-length(p) with which the Information Criteria were computed.

References

Cerioli, A., Garcia-Escudero, L.A., Mayo-Iscar, A. and Riani M. (2017). Finding the Number of Groups in Model-Based Clustering via Constrained Likelihoods, Journal of Computational and Graphical Statistics, pp. 404-416, https://doi.org/10.1080/10618600.2017.1390469.

See Also

tclust

Examples

 #--- EXAMPLE 1 ------------------------------------------
 
 data(geyser2)
 (out <- tclustIC(geyser2, whichIC="MIXMIX", alpha=0.1))
 summary(out)
 ## Find the smallest value inside the table and write the corresponding
 ## values of k (number of groups) and c (restriction factor)
 inds <- which(out$MIXMIX == min(out$MIXMIX), arr.ind=TRUE)
 vals <- out$MIXMIX[inds]
 cat("\nThe smallest value of the IC is ", vals, 
     " and takes place for k=", out$kk[inds[1]], " and c=",   
     out$cc[inds[2]], "\n")
 

 #--- EXAMPLE 2 ------------------------------------------
 
 data(flea)
 Y <- as.matrix(flea[, 1:(ncol(flea)-1)])    # select only the numeric variables
 rownames(Y) <- 1:nrow(Y)
 head(Y)

 (out <- tclustIC(Y, whichIC="CLACLA", alpha=0.1))
 summary(out)
 ## Find the smallest value inside the table and write the corresponding
 ## values of k (number of groups) and c (restriction factor)
 inds <- which(out$CLACLA == min(out$CLACLA), arr.ind=TRUE)
 vals <- out$CLACLA[inds]
 cat("\nThe Smallest value of the IC is ", vals, 
     " and takes place for k=", out$kk[inds[1]], " and c=",   
     out$cc[inds[2]], "\n")
 

 #--- EXAMPLE 3 ------------------------------------------
 
 data(swissbank)
 (out <- tclustIC(swissbank, whichIC="ALL"))
 
 plot(out)  ##  --> selecting k=3, c=128
 
 ##  the selected model
 plot(tclust(swissbank, k = 3, alpha = 0.1, restr.fact = 128))
 
 

Extracts a set of best relevant solutions obtained by tclustIC

Description

The function tclustICsol() takes as input an object of class tclustIC, the output of function tclustIC (that is a series of matrices which contain the values of the information criteria BIC/ICL/CLA for different values of k and c) and extracts the first best solutions. Two solutions are considered equivalent if the value of the adjusted Rand index (or the adjusted Fowlkes and Mallows index) is above a certain threshold. For each tentative solution the program checks the adjacent values of c for which the solution is stable. A matrix with adjusted Rand indexes is given for the extracted solutions.

Usage

tclustICsol(
  obj,
  whichIC = c("ALL", "MIXMIX", "MIXCLA", "CLACLA"),
  nsol = 5,
  index = c("Rand", "FM"),
  thresholdRI = 0.7,
  trace = FALSE
)

Arguments

Value

The function returns an S3 object of type tclustICsol containing the following components:

References

Cerioli, A., Garcia-Escudero, L.A., Mayo-Iscar, A. and Riani M. (2017). Finding the Number of Groups in Model-Based Clustering via Constrained Likelihoods, Journal of Computational and Graphical Statistics, pp. 404–416, https://doi.org/10.1080/10618600.2017.1390469.

Hubert L. and Arabie P. (1985), Comparing Partitions, Journal of Classification, Vol. 2, pp. 193–218.

See Also

tclust, tclustIC

Examples

 #--- EXAMPLE 1 ------------------------------------------
 
 data(geyser2)
 (out <- tclustIC(geyser2, whichIC="MIXMIX", alpha=0.1))

 ##  Show the first two best solutions using as Information criterion MIXMIX
 cat("\nBest solutions using MIXMIX\n")
 outsol <- tclust::tclustICsol(out, whichIC="MIXMIX", nsol=2)
 print(outsol$MIXMIXbs)
 

 #--- EXAMPLE 2 ------------------------------------------
 
 data(flea)
 Y <- as.matrix(flea[, 1:(ncol(flea)-1)])    # select only the numeric variables
 rownames(Y) <- 1:nrow(Y)
 head(Y)

 (out <- tclustIC(Y, whichIC="CLACLA", alpha=0.1))
 ## Find the smallest value inside the table and write the corresponding
 ## values of k (number of groups) and c (restriction factor)
 inds <- which(out$CLACLA == min(out$CLACLA), arr.ind=TRUE)
 vals <- out$CLACLA[inds]
 cat("\nThe Smallest value of the IC is ", vals, 
     " and takes place for k=", out$kk[inds[1]], " and c=",   
     out$cc[inds[2]], "\n")

 ##  Show the first two best solutions using as Information criterion CLACLA
 cat("\nBest solutions using CLACLA\n")
 outsol <- tclust::tclustICsol(out, whichIC="CLACLA", nsol=2)
 print(outsol$CLACLAbs)
 
 

 #--- EXAMPLE 3 ------------------------------------------
 
 data(swissbank)
 (out <- tclustIC(swissbank, whichIC="ALL"))
 
 outsol <- tclust::tclustICsol(out, whichIC="ALL", nsol=2)
 print(outsol$CLACLAbs)
 
 

TKMEANS method for robust K-means clustering

Description

This function searches for k (or less) spherical clusters in a data matrix x, whereas the ceiling(alpha n) most outlying observations are trimmed.

Usage

tkmeans(
  x,
  k,
  alpha = 0.05,
  nstart = 500,
  niter1 = 3,
  niter2 = 20,
  nkeep = 5,
  iter.max,
  points = NULL,
  center = FALSE,
  scale = FALSE,
  store_x = TRUE,
  parallel = FALSE,
  n.cores = -1,
  zero_tol = 1e-16,
  drop.empty.clust = TRUE,
  trace = 0
)

Arguments

Value

The function returns the following values:

• cluster - A numerical vector of size n containing the cluster assignment for each observation. Cluster names are integer numbers from 1 to k, 0 indicates trimmed observations. Note that it could be empty clusters with no observations when equal.weights=FALSE.

• obj - The value of the objective function of the best (returned) solution.

• size - An integer vector of size k, returning the number of observations contained by each cluster.

• centers - A matrix of size p x k containing the centers (column-wise) of each cluster.

• code - A numerical value indicating if the concentration steps have converged for the returned solution (2).

• cluster.ini - A matrix with nstart rows and number of columns equal to the number of observations and where each row shows the final clustering assignments (0 for trimmed observations) obtained after the niter1 iteration of the nstart random initializations.

• obj.ini - A numerical vector of length nstart containing the values of the target function obtained after the niter1 iteration of the nstart random initializations.

• x - The input data set.

• k - The input number of clusters.

• alpha - The input trimming level.

Author(s)

Valentin Todorov, Luis Angel Garcia Escudero, Agustin Mayo Iscar.

References

Cuesta-Albertos, J. A.; Gordaliza, A. and Matrán, C. (1997), "Trimmed k-means: an attempt to robustify quantizers". Annals of Statistics, Vol. 25 (2), 553-576.

Examples

 
 ##--- EXAMPLE 1 ------------------------------------------
 sig <- diag(2)
 cen <- rep(1,2)
 x <- rbind(MASS::mvrnorm(360, cen * 0,   sig),
            MASS::mvrnorm(540, cen * 5,   sig),
            MASS::mvrnorm(100, cen * 2.5, sig))
 
 ## Two groups and 10\% trimming level
 (clus <- tkmeans(x, k = 2, alpha = 0.1))

 plot(clus)
 plot(clus, labels = "observation")
 plot(clus, labels = "cluster")

 #--- EXAMPLE 2 ------------------------------------------
 data(geyser2)
 (clus <- tkmeans(geyser2, k = 3, alpha = 0.03))
 plot(clus)
 

Wholesale customers dataset

Description

The data set refers to clients of a wholesale distributor. It includes the annual spending in monetary units on diverse product categories.

Usage

data(wholesale)

Format

A data frame containing 440 observations in 8 variables (6 numerical and two categorical). The variables are as follows:

• Region Customers' Region - Lisbon (coded as 1), Porto (coded as 2) or Other (coded as 3)

• Fresh Annual spending on fresh products

• Milk Annual spending on milk products

• Grocery Annual spending on grocery products

• Frozen Annual spending on frozen products

• Detergents Annual spending on detergents and paper products

• Delicatessen Annual spending on and delicatessen products

• Channel Customers' Channel - Horeca (Hotel/Restaurant/Café) or Retail channel. Horeca is coded as 1 and Retail channel is coded as 2

Source

Abreu, N. (2011). Analise do perfil do cliente Recheio e desenvolvimento de um sistema promocional. Mestrado em Marketing, ISCTE-IUL, Lisbon. url=https://api.semanticscholar.org/CorpusID:124027622

Examples

#--- EXAMPLE 1 ------------------------------------------ 
data (wholesale) 
x <- wholesale[, -c(1, ncol(wholesale))] 
clus <- tclust(x, k=3, alpha=0.1, nstart=200, niter1=3, niter2=17, 
   nkeep=10, opt="HARD", equal.weights=FALSE, restr.fact=50, trace=TRUE) 
 plot (x, col=clus$cluster+1)
 plot(clus)