WH_fcmeans: Fuzzy c-means of a dataset of histogram-valued data

In HistDAWass: Histogram-Valued Data Analysis

Fuzzy c-means of a dataset of histogram-valued data

Description

The function implements the fuzzy c-means for a set of histogram-valued data.

Usage

WH_fcmeans(x, k, m = 1.6, rep, simplify = FALSE, qua = 10, standardize = FALSE)

Arguments

x	A MatH object (a matrix of distributionH).
k	An integer, the number of groups.
m	A number grater than 0, a fuzziness coefficient (default m=1.6).
rep	An integer, maximum number of repetitions of the algorithm (default rep=5).
simplify	A logic value (default is FALSE), if TRUE histograms are recomputed in order to speed-up the algorithm.
qua	An integer, if simplify=TRUE is the number of quantiles used for recodify the histograms.
standardize	A logic value (default is FALSE). If TRUE, histogram-valued data are standardized, variable by variable, using the Wassertein based standard deviation. Use if one wants to have variables with std equal to one.

Value

a list with the results of the fuzzy c-means of the set of Histogram-valued data x into k cluster.

Slots

solution

A list.Returns the best solution among the repetitions, i.e. the one having the minimum sum of squares deviation.

solution$membership

A matrix. The membership degree of each unit to each cluster.

solution$IDX

A vector. The crisp assignement to a cluster.

solution$cardinality

A vector. The cardinality of each final cluster (after the crisp assignement).

solution$Crit

A number. The criterion (Sum of square deviation from the prototypes) value at the end of the run.

quality

A number. The percentage of Sum of square deviation explained by the model. (The higher the better)

Examples

results <- WH_fcmeans(x = BLOOD, k = 2, m = 1.5, rep = 10, 
                      simplify = TRUE, qua = 10, standardize = TRUE)

HistDAWass documentation built on Sept. 26, 2022, 5:06 p.m.