| Title: | Simulating Data to Study Performance of Clustering Algorithms |
|---|---|
| Description: | The utility of this package is in simulating mixtures of Gaussian distributions with different levels of overlap between mixture components. Pairwise overlap, defined as a sum of two misclassification probabilities, measures the degree of interaction between components and can be readily employed to control the clustering complexity of datasets simulated from mixtures. These datasets can then be used for systematic performance investigation of clustering and finite mixture modeling algorithms. Among other capabilities of 'MixSim', there are computing the exact overlap for Gaussian mixtures, simulating Gaussian and non-Gaussian data, simulating outliers and noise variables, calculating various measures of agreement between two partitionings, and constructing parallel distribution plots for the graphical display of finite mixture models. |
| Authors: | Volodymyr Melnykov [aut], Wei-Chen Chen [aut, cre], Ranjan Maitra [aut], Robert Davies [ctb] (quadratic form probabilities), Stephen Moshier [ctb] (eigenvalue calculations), Rouben Rostamian [ctb] (memory allocation) |
| Maintainer: | Wei-Chen Chen <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.1-8 |
| Built: | 2024-11-15 06:24:02 UTC |
| Source: | CRAN |
Simulation of Gaussian finite mixture models for prespecified levels of average or/and maximum overlap. Pairwise overlap is defined as the sum of two misclassification probabilities.
Function 'MixSim' simulates a finite mixture model for a prespecified level of average or/and maximum overlap.
Function 'overlap' computes all misclassification probabilities for a finite mixture model.
Function 'pdplot' constructs a parallel distribution plot for a finite mixture model.
Function 'simdataset' simulates a dataset from a finite mixture model.
Volodymyr Melnykov, Wei-Chen Chen, and Ranjan Maitra.
Maintainer: Volodymyr Melnykov <[email protected]>
Maitra, R. and Melnykov, V. (2010) “Simulating data to study performance of finite mixture modeling and clustering algorithms”, The Journal of Computational and Graphical Statistics, 2:19, 354-376.
Melnykov, V., Chen, W.-C., and Maitra, R. (2012) “MixSim: An R Package for Simulating Data to Study Performance of Clustering Algorithms”, Journal of Statistical Software, 51:12, 1-25.
Davies, R. (1980) “The distribution of a linear combination of chi-square random variables”, Applied Statistics, 29, 323-333.
Meila, M. (2006) “Comparing clusterings - an information based distance”, Journal of Multivariate Analysis, 98, 873-895.
# Simulate parameters of a mixture model A <- MixSim(BarOmega = 0.01, MaxOmega = 0.10, K = 10, p = 5) # Display the mixture via the parallel distribution plot pdplot(A$Pi, A$Mu, A$S, MaxInt = 0.5)# Simulate parameters of a mixture model A <- MixSim(BarOmega = 0.01, MaxOmega = 0.10, K = 10, p = 5) # Display the mixture via the parallel distribution plot pdplot(A$Pi, A$Mu, A$S, MaxInt = 0.5)
Computes the agreement proportion between two classification vectors.
ClassProp(id1, id2)ClassProp(id1, id2)
id1 |
first partitioning vector. |
id2 |
second partitioning vector. |
Returns the value of the proportion of agreeing elements.
Volodymyr Melnykov, Wei-Chen Chen, and Ranjan Maitra.
Meila, M. (2006) “Comparing clusterings - an information based distance”, Journal of Multivariate Analysis, 98, 873-895.
RandIndex, and VarInf.
id1 <- c(rep(1, 50), rep(2,100)) id2 <- rep(1:3, each = 50) ClassProp(id1, id2)id1 <- c(rep(1, 50), rep(2,100)) id2 <- rep(1:3, each = 50) ClassProp(id1, id2)
Generates a finite mixture model with Gaussian components for a prespecified level of goMega (generalized overlap of Maitra).
MixGOM(goMega = NULL, K, p, sph = FALSE, hom = FALSE, ecc = 0.90, PiLow = 1.0, int = c(0.0, 1.0), resN = 100, eps = 1e-06, lim = 1e06)MixGOM(goMega = NULL, K, p, sph = FALSE, hom = FALSE, ecc = 0.90, PiLow = 1.0, int = c(0.0, 1.0), resN = 100, eps = 1e-06, lim = 1e06)
goMega |
value of desired generalized overlap of Maitra. |
K |
number of components. |
p |
number of dimensions. |
sph |
covariance matrix structure (FALSE - non-spherical, TRUE - spherical). |
hom |
heterogeneous or homogeneous clusters (FALSE - heterogeneous, TRUE - homogeneous). |
ecc |
maximum eccentricity. |
PiLow |
value of the smallest mixing proportion (if 'PiLow' is not reachable with respect to K, equal proportions are taken; PiLow = 1.0 implies equal proportions by default). |
int |
mean vectors are simulated uniformly on a hypercube with sides specified by int = (lower.bound, upper.bound). |
resN |
maximum number of mixture resimulations. |
eps |
error bound for overlap computation. |
lim |
maximum number of integration terms (Davies, 1980). |
Returns mixture parameters satisfying the prespecified level of goMega.
Pi |
vector of mixing proportions. |
Mu |
matrix consisting of components' mean vectors (K * p). |
S |
set of components' covariance matrices (p * p * K). |
goMega |
value of generalized overlap of Maitra. |
fail |
flag value; 0 represents successful mixture generation, 1 represents failure. |
Volodymyr Melnykov, Wei-Chen Chen, and Ranjan Maitra.
Maitra, R. (2010) “A re-defined and generalized percent-overlap-of-activation measure for studies of fMRI reproducibility and its use in identifying outlier activation maps”, NeuroImage, 50, 124-135.
Maitra, R. and Melnykov, V. (2010) “Simulating data to study performance of finite mixture modeling and clustering algorithms”, The Journal of Computational and Graphical Statistics, 2:19, 354-376.
Melnykov, V., Chen, W.-C., and Maitra, R. (2012) “MixSim: An R Package for Simulating Data to Study Performance of Clustering Algorithms”, Journal of Statistical Software, 51:12, 1-25.
Davies, R. (1980) “The distribution of a linear combination of chi-square random variables”, Applied Statistics, 29, 323-333.
overlapGOM, MixSim, and simdataset.
set.seed(1234) # controls average and maximum overlaps (ex.1 <- MixGOM(goMega = 0.05, K = 4, p = 5)) # controls maximum overlap (ex.2 <- MixGOM(goMega = 0.15, K = 4, p = 5, sph = TRUE))set.seed(1234) # controls average and maximum overlaps (ex.1 <- MixGOM(goMega = 0.05, K = 4, p = 5)) # controls maximum overlap (ex.2 <- MixGOM(goMega = 0.15, K = 4, p = 5, sph = TRUE))
Generates a finite mixture model with Gaussian components for prespecified levels of maximum and/or average overlaps.
MixSim(BarOmega = NULL, MaxOmega = NULL, K, p, sph = FALSE, hom = FALSE, ecc = 0.90, PiLow = 1.0, int = c(0.0, 1.0), resN = 100, eps = 1e-06, lim = 1e06)MixSim(BarOmega = NULL, MaxOmega = NULL, K, p, sph = FALSE, hom = FALSE, ecc = 0.90, PiLow = 1.0, int = c(0.0, 1.0), resN = 100, eps = 1e-06, lim = 1e06)
BarOmega |
value of desired average overlap. |
MaxOmega |
value of desired maximum overlap. |
K |
number of components. |
p |
number of dimensions. |
sph |
covariance matrix structure (FALSE - non-spherical, TRUE - spherical). |
hom |
heterogeneous or homogeneous clusters (FALSE - heterogeneous, TRUE - homogeneous). |
ecc |
maximum eccentricity. |
PiLow |
value of the smallest mixing proportion (if 'PiLow' is not reachable with respect to K, equal proportions are taken; PiLow = 1.0 implies equal proportions by default). |
int |
mean vectors are simulated uniformly on a hypercube with sides specified by int = (lower.bound, upper.bound). |
resN |
maximum number of mixture resimulations. |
eps |
error bound for overlap computation. |
lim |
maximum number of integration terms (Davies, 1980). |
If 'BarOmega' is not specified, the function generates a mixture solely based on 'MaxOmega'; if 'MaxOmega' is not specified, the function generates a mixture solely based on 'BarOmega'.
If 'hom' is TRUE, only one of 'BarOmega' or 'MaxOmega' can be specified.
Pi |
vector of mixing proportions. |
Mu |
matrix consisting of components' mean vectors (K * p). |
S |
set of components' covariance matrices (p * p * K). |
OmegaMap |
matrix of misclassification probabilities (K * K); OmegaMap[i,j] is the probability that X coming from the i-th component is classified to the j-th component. |
BarOmega |
value of average overlap. |
MaxOmega |
value of maximum overlap. |
rcMax |
row and column numbers for the pair of components producing maximum overlap 'MaxOmega'. |
fail |
flag value; 0 represents successful mixture generation, 1 represents failure. |
Volodymyr Melnykov, Wei-Chen Chen, and Ranjan Maitra.
Maitra, R. and Melnykov, V. (2010) “Simulating data to study performance of finite mixture modeling and clustering algorithms”, The Journal of Computational and Graphical Statistics, 2:19, 354-376.
Melnykov, V., Chen, W.-C., and Maitra, R. (2012) “MixSim: An R Package for Simulating Data to Study Performance of Clustering Algorithms”, Journal of Statistical Software, 51:12, 1-25.
Davies, R. (1980) “The distribution of a linear combination of chi-square random variables”, Applied Statistics, 29, 323-333.
overlap, pdplot, and simdataset.
set.seed(1234) # controls average and maximum overlaps (ex.1 <- MixSim(BarOmega = 0.05, MaxOmega = 0.15, K = 4, p = 5)) summary(ex.1) # controls average overlap (ex.2 <- MixSim(BarOmega = 0.05, K = 4, p = 5, hom = TRUE)) summary(ex.2) # controls maximum overlap (ex.3 <- MixSim(MaxOmega = 0.15, K = 4, p = 5, sph = TRUE)) summary(ex.3)set.seed(1234) # controls average and maximum overlaps (ex.1 <- MixSim(BarOmega = 0.05, MaxOmega = 0.15, K = 4, p = 5)) summary(ex.1) # controls average overlap (ex.2 <- MixSim(BarOmega = 0.05, K = 4, p = 5, hom = TRUE)) summary(ex.2) # controls maximum overlap (ex.3 <- MixSim(MaxOmega = 0.15, K = 4, p = 5, sph = TRUE)) summary(ex.3)
Computes misclassification probabilities and pairwise overlaps for finite mixture models with Gaussian components. Overlap is defined as sum of two misclassification probabilities.
overlap(Pi, Mu, S, eps = 1e-06, lim = 1e06)overlap(Pi, Mu, S, eps = 1e-06, lim = 1e06)
Pi |
vector of mixing proprtions (length K). |
Mu |
matrix consisting of components' mean vectors (K * p). |
S |
set of components' covariance matrices (p * p * K). |
eps |
error bound for overlap computation. |
lim |
maximum number of integration terms (Davies, 1980). |
OmegaMap |
matrix of misclassification probabilities (K * K); OmegaMap[i,j] is the probability that X coming from the i-th component is classified to the j-th component. |
BarOmega |
value of average overlap. |
MaxOmega |
value of maximum overlap. |
rcMax |
row and column numbers for the pair of components producing maximum overlap 'MaxOmega'. |
Volodymyr Melnykov, Wei-Chen Chen, and Ranjan Maitra.
Maitra, R. and Melnykov, V. (2010) “Simulating data to study performance of finite mixture modeling and clustering algorithms”, The Journal of Computational and Graphical Statistics, 2:19, 354-376.
Melnykov, V., Chen, W.-C., and Maitra, R. (2012) “MixSim: An R Package for Simulating Data to Study Performance of Clustering Algorithms”, Journal of Statistical Software, 51:12, 1-25.
Davies, R. (1980) “The distribution of a linear combination of chi-square random variables”, Applied Statistics, 29, 323-333.
MixSim, pdplot, and simdataset.
data("iris", package = "datasets") p <- ncol(iris) - 1 id <- as.integer(iris[, 5]) K <- max(id) # estimate mixture parameters Pi <- prop.table(tabulate(id)) Mu <- t(sapply(1:K, function(k){ colMeans(iris[id == k, -5]) })) S <- sapply(1:K, function(k){ var(iris[id == k, -5]) }) dim(S) <- c(p, p, K) overlap(Pi = Pi, Mu = Mu, S = S)data("iris", package = "datasets") p <- ncol(iris) - 1 id <- as.integer(iris[, 5]) K <- max(id) # estimate mixture parameters Pi <- prop.table(tabulate(id)) Mu <- t(sapply(1:K, function(k){ colMeans(iris[id == k, -5]) })) S <- sapply(1:K, function(k){ var(iris[id == k, -5]) }) dim(S) <- c(p, p, K) overlap(Pi = Pi, Mu = Mu, S = S)
Computes the generalized overlap as defined by R. Maitra.
overlapGOM(Pi, Mu, S, eps = 1e-06, lim = 1e06)overlapGOM(Pi, Mu, S, eps = 1e-06, lim = 1e06)
Pi |
vector of mixing proprtions (length K). |
Mu |
matrix consisting of components' mean vectors (K * p). |
S |
set of components' covariance matrices (p * p * K). |
eps |
error bound for overlap computation. |
lim |
maximum number of integration terms (Davies, 1980). |
Returns the value of goMega.
Volodymyr Melnykov, Wei-Chen Chen, and Ranjan Maitra.
Maitra, R. (2010) “A re-defined and generalized percent-overlap-of-activation measure for studies of fMRI reproducibility and its use in identifying outlier activation maps”, NeuroImage, 50, 124-135.
Melnykov, V., Chen, W.-C., and Maitra, R. (2012) “MixSim: An R Package for Simulating Data to Study Performance of Clustering Algorithms”, Journal of Statistical Software, 51:12, 1-25.
Davies, R. (1980) “The distribution of a linear combination of chi-square random variables”, Applied Statistics, 29, 323-333.
MixSim, MixGOM, and overlap.
data("iris", package = "datasets") p <- ncol(iris) - 1 id <- as.integer(iris[, 5]) K <- max(id) # estimate mixture parameters Pi <- prop.table(tabulate(id)) Mu <- t(sapply(1:K, function(k){ colMeans(iris[id == k, -5]) })) S <- sapply(1:K, function(k){ var(iris[id == k, -5]) }) dim(S) <- c(p, p, K) overlapGOM(Pi = Pi, Mu = Mu, S = S)data("iris", package = "datasets") p <- ncol(iris) - 1 id <- as.integer(iris[, 5]) K <- max(id) # estimate mixture parameters Pi <- prop.table(tabulate(id)) Mu <- t(sapply(1:K, function(k){ colMeans(iris[id == k, -5]) })) S <- sapply(1:K, function(k){ var(iris[id == k, -5]) }) dim(S) <- c(p, p, K) overlapGOM(Pi = Pi, Mu = Mu, S = S)
Constructs a parallel distribution plot for Gaussian finite mixture models.
pdplot(Pi, Mu, S, file = NULL, Nx = 5, Ny = 5, MaxInt = 1, marg = c(2,1,1,1))pdplot(Pi, Mu, S, file = NULL, Nx = 5, Ny = 5, MaxInt = 1, marg = c(2,1,1,1))
Pi |
vector of mixing proportions. |
Mu |
matrix consisting of components' mean vectors (K * p). |
S |
set of components' covariance matrices (p * p * K). |
file |
name of .pdf-file. |
Nx |
number of color levels for smoothing along the x-axis. |
Ny |
number of color levels for smoothing along the y-axis. |
MaxInt |
maximum color intensity. |
marg |
plot margins. |
If 'file' is specified, produced plot will be saved as a .pdf-file.
Volodymyr Melnykov, Wei-Chen Chen, and Ranjan Maitra.
Maitra, R. and Melnykov, V. (2010) “Simulating data to study performance of finite mixture modeling and clustering algorithms”, The Journal of Computational and Graphical Statistics, 2:19, 354-376.
Melnykov, V., Chen, W.-C., and Maitra, R. (2012) “MixSim: An R Package for Simulating Data to Study Performance of Clustering Algorithms”, Journal of Statistical Software, 51:12, 1-25.
MixSim, overlap, and simdataset.
data("iris", package = "datasets") p <- ncol(iris) - 1 id <- as.integer(iris[, 5]) K <- max(id) # estimate mixture parameters Pi <- prop.table(tabulate(id)) Mu <- t(sapply(1:K, function(k){ colMeans(iris[id == k, -5]) })) S <- sapply(1:K, function(k){ var(iris[id == k, -5]) }) dim(S) <- c(p, p, K) pdplot(Pi = Pi, Mu = Mu, S = S)data("iris", package = "datasets") p <- ncol(iris) - 1 id <- as.integer(iris[, 5]) K <- max(id) # estimate mixture parameters Pi <- prop.table(tabulate(id)) Mu <- t(sapply(1:K, function(k){ colMeans(iris[id == k, -5]) })) S <- sapply(1:K, function(k){ var(iris[id == k, -5]) }) dim(S) <- c(p, p, K) pdplot(Pi = Pi, Mu = Mu, S = S)
Returns all possible permutations given the number of elements.
perms(n)perms(n)
n |
Number of elements. |
Returns a matrix containing all possible permutations.
Volodymyr Melnykov, Wei-Chen Chen, and Ranjan Maitra.
ClassProp.
perms(3)perms(3)
A MixSim and MixGOM classes are declared, and these are functions
to print and summarize objects.
## S3 method for class 'MixSim' print(x, ...) ## S3 method for class 'MixSim' summary(object, ...) ## S3 method for class 'MixGOM' print(x, ...)## S3 method for class 'MixSim' print(x, ...) ## S3 method for class 'MixSim' summary(object, ...) ## S3 method for class 'MixGOM' print(x, ...)
x |
an object with the 'MixSim' (or 'MixGOM') class attributes. |
object |
an object with the 'MixSim' (or 'MixGOM') class attributes. |
... |
other possible options. |
These are useful functions for summarizing and debugging.
For other functions, they only show summaries of objects. Use
names or str to explore the details.
The results will cat or print on the STDOUT by default.
Volodymyr Melnykov, Wei-Chen Chen, and Ranjan Maitra.
Maitra, R. and Melnykov, V. (2010) “Simulating data to study performance of finite mixture modeling and clustering algorithms”, The Journal of Computational and Graphical Statistics, 2:19, 354-376.
Melnykov, V., Chen, W.-C., and Maitra, R. (2012) “MixSim: An R Package for Simulating Data to Study Performance of Clustering Algorithms”, Journal of Statistical Software, 51:12, 1-25.
## Not run: # Functions applied by directly type the names of objects. ## End(Not run)## Not run: # Functions applied by directly type the names of objects. ## End(Not run)
Computes Rand, adjusted Rand, Fowlkes and Mallows, and Merkin indices.
RandIndex(id1, id2)RandIndex(id1, id2)
id1 |
first partitioning vector. |
id2 |
second partitioning vector. |
R |
Rand's index. |
AR |
adjusted Rand's index. |
F |
Fowlkes and Mallows index. |
M |
Mirkin metric. |
Volodymyr Melnykov, Wei-Chen Chen, and Ranjan Maitra.
Rand, W.M. (1971) “Objective criteria for the evaluation of clustering methods”, Journal of the American Statistical Association, 66:336, 846-850.
Maitra, R. and Melnykov, V. (2010) “Simulating data to study performance of finite mixture modeling and clustering algorithms”, The Journal of Computational and Graphical Statistics, 2:19, 354-376.
Meila, M. (2006) “Comparing clusterings - an information based distance”, Journal of Multivariate Analysis, 98, 873-895.
Melnykov, V., Chen, W.-C., and Maitra, R. (2012) “MixSim: An R Package for Simulating Data to Study Performance of Clustering Algorithms”, Journal of Statistical Software, 51:12, 1-25.
MixSim, pdplot, simdataset, ClassProp,
and VarInf.
id1 <- c(rep(1, 50), rep(2,100)) id2 <- rep(1:3, each = 50) RandIndex(id1, id2)id1 <- c(rep(1, 50), rep(2,100)) id2 <- rep(1:3, each = 50) RandIndex(id1, id2)
Simulates a datasets of sample size n given parameters of finite mixture model with Gaussian components.
simdataset(n, Pi, Mu, S, n.noise = 0, n.out = 0, alpha = 0.001, max.out = 100000, int = NULL, lambda = NULL)simdataset(n, Pi, Mu, S, n.noise = 0, n.out = 0, alpha = 0.001, max.out = 100000, int = NULL, lambda = NULL)
n |
sample size. |
Pi |
vector of mixing proportions (length K). |
Mu |
matrix consisting of components' mean vectors (K * p). |
S |
set of components' covariance matrices (p * p * K). |
n.noise |
number of noise variables. |
n.out |
number of outlying observations. |
alpha |
level for simulating outliers. |
max.out |
maximum number of trials to simulate outliers. |
int |
interval for noise and outlier generation. |
lambda |
inverse Box-Cox transformation coefficients. |
The function simulates a dataset of n observations from a mixture model with parameters 'Pi' (mixing proportions), 'Mu' (mean vectors), and 'S' (covariance matrices). Mixture component sample sizes are produced as a realization from a multinomial distribution with probabilities given by mixing proportions. To make a dataset more challenging for clustering, a user might want to simulate noise variables or outliers. Parameter 'n.noise' specifies the desired number of noise variables. If an interval 'int' is specified, noise will be simulated from a Uniform distribution on the interval given by 'int'. Otherwise, noise will be simulated uniformly between the smallest and largest coordinates of mean vectors. 'n.out' specifies the number of observations outside (1 - 'alpha') ellipsoidal contours for the weighted component distributions. Outliers are simulated on a hypercube specified by the interval 'int'. A user can apply an inverse Box-Cox transformation providing a vector of coefficients 'lambda'. The value 1 implies that no transformation is needed for the corresponding coordinate.
X |
simulated dataset (n + n.out) x (p + n.noise); noise coordinates are provided in the last n.noise columns. |
id |
classification vector (length n + n.out); 0 represents an outlier. |
Volodymyr Melnykov, Wei-Chen Chen, and Ranjan Maitra.
Maitra, R. and Melnykov, V. (2010) “Simulating data to study performance of finite mixture modeling and clustering algorithms”, The Journal of Computational and Graphical Statistics, 2:19, 354-376.
Melnykov, V., Chen, W.-C., and Maitra, R. (2012) “MixSim: An R Package for Simulating Data to Study Performance of Clustering Algorithms”, Journal of Statistical Software, 51:12, 1-25.
MixSim, overlap, and pdplot.
## Not run: set.seed(1234) repeat{ Q <- MixSim(BarOmega = 0.01, K = 4, p = 2) if (Q$fail == 0) break } # simulate a dataset of size 300 and add 10 outliers simulated on (0,1)x(0,1) A <- simdataset(n = 500, Pi = Q$Pi, Mu = Q$Mu, S = Q$S, n.out = 10, int = c(0, 1)) colors <- c("red", "green", "blue", "brown", "magenta") plot(A$X, xlab = "x1", ylab = "x2", type = "n") for (k in 0:4){ points(A$X[A$id == k, ], col = colors[k+1], pch = 19, cex = 0.5) } repeat{ Q <- MixSim(MaxOmega = 0.1, K = 4, p = 1) if (Q$fail == 0) break } # simulate a dataset of size 300 with 1 noise variable A <- simdataset(n = 300, Pi = Q$Pi, Mu = Q$Mu, S = Q$S, n.noise = 1) plot(A$X, xlab = "x1", ylab = "x2", type = "n") for (k in 1:4){ points(A$X[A$id == k, ], col = colors[k+1], pch = 19, cex = 0.5) } ## End(Not run)## Not run: set.seed(1234) repeat{ Q <- MixSim(BarOmega = 0.01, K = 4, p = 2) if (Q$fail == 0) break } # simulate a dataset of size 300 and add 10 outliers simulated on (0,1)x(0,1) A <- simdataset(n = 500, Pi = Q$Pi, Mu = Q$Mu, S = Q$S, n.out = 10, int = c(0, 1)) colors <- c("red", "green", "blue", "brown", "magenta") plot(A$X, xlab = "x1", ylab = "x2", type = "n") for (k in 0:4){ points(A$X[A$id == k, ], col = colors[k+1], pch = 19, cex = 0.5) } repeat{ Q <- MixSim(MaxOmega = 0.1, K = 4, p = 1) if (Q$fail == 0) break } # simulate a dataset of size 300 with 1 noise variable A <- simdataset(n = 300, Pi = Q$Pi, Mu = Q$Mu, S = Q$S, n.noise = 1) plot(A$X, xlab = "x1", ylab = "x2", type = "n") for (k in 1:4){ points(A$X[A$id == k, ], col = colors[k+1], pch = 19, cex = 0.5) } ## End(Not run)
Computes the variation of information for two classification vectors.
VarInf(id1, id2)VarInf(id1, id2)
id1 |
first partitioning vector. |
id2 |
second partitioning vector. |
Returns the variation of information. It is equal to 0 if and only if two classification vectors are identical.
Volodymyr Melnykov, Wei-Chen Chen, and Ranjan Maitra.
Meila, M. (2006) “Comparing clusterings - an information based distance”, Journal of Multivariate Analysis, 98, 873-895.
ClassProp, and RandIndex.
id1 <- c(rep(1, 50), rep(2,100)) id2 <- rep(1:3, each = 50) VarInf(id1, id2)id1 <- c(rep(1, 50), rep(2,100)) id2 <- rep(1:3, each = 50) VarInf(id1, id2)