| alg.CU {QuClu} | R Documentation |
CU quantile-based clustering algorithm
Description
This function allows to run the CU (Common theta and Unscaled variables) version of the quantile-based clustering algorithm.
Usage
alg.CU(data, k = 2, eps = 1e-08, it.max = 100, B = 30)
Arguments
data |
A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. |
k |
The number of clusters. The default is k=2. |
eps |
The relative convergence tolerances for objective function. The default is set to 1e-8. |
it.max |
A number that gives integer limits on the number of the CU algorithm iterations. By default, it is set to 100. |
B |
The number of times the initialization step is repeated; the default is 30. |
Details
Algorithm CU: Common theta and Unscaled variables. A common value of theta for all the variables is assumed. This strategy directly generalizes the conventional k-means to other moments of the distribution to better accommodate skewness in the data.
Value
A list containing the following elements:
method |
The chosen parameterization, CU, Common theta and Unscaled variables |
k |
The number of clusters. |
cl |
A vector whose [i]th entry is classification of observation i in the test data. |
qq |
A matrix whose [h,j]th entry is the theta-quantile of variable j in cluster h. |
theta |
A vector whose [j]th entry is the percentile theta for variable j. |
Vseq |
The values of the objective function V at each step of the algorithm. |
V |
The final value of the objective function V. |
lambda |
A vector containing the scaling factor for each variable. |
References
Hennig, C., Viroli, C., Anderlucci, L. (2019) "Quantile-based clustering" Electronic Journal of Statistics, 13 (2) 4849-4883 <doi:10.1214/19-EJS1640>
Examples
out <- alg.CU(iris[,-5],k=3)
out$theta
out$qq
table(out$cl)