FPDC | R Documentation |
An implementation of FPDC, a probabilistic factor clustering algorithm that involves a linear transformation of variables and a cluster optimizing the PD-clustering criterion
FPDC(data = NULL, k = 2, nf = 2, nu = 2)
data |
A matrix or data frame such that rows correspond to observations and columns correspond to variables. |
k |
A numerical parameter giving the number of clusters |
nf |
A numerical parameter giving the number of factors for variables |
nu |
A numerical parameter giving the number of factors for units |
A class FPDclustering list with components
label |
A vector of integers indicating the cluster membership for each unit |
centers |
A matrix of cluster centers |
probability |
A matrix of probability of each point belonging to each cluster |
JDF |
The value of the Joint distance function |
iter |
The number of iterations |
explained |
The explained variability |
data |
the data set |
Cristina Tortora and Paul D. McNicholas
Tortora, C., M. Gettler Summa, M. Marino, and F. Palumbo. Factor probabilistic distance clustering (fpdc): a new clustering method for high dimensional data sets. Advanced in Data Analysis and Classification, 10(4), 441-464, 2016. doi:10.1007/s11634-015-0219-5.
Tortora C., Gettler Summa M., and Palumbo F.. Factor pd-clustering. In Lausen et al., editor, Algorithms from and for Nature and Life, Studies in Classification, Data Analysis, and Knowledge Organization DOI 10.1007/978-3-319-00035-011, 115-123, 2013.
Tortora C., Non-hierarchical clustering methods on factorial subspaces, 2012.
PDC
# Asymmetric data set clustering example (with shape 3).
data('asymmetric3')
x<-asymmetric3[,-1]
#Clustering
fpdas3=FPDC(x,4,3,3)
#Results
table(asymmetric3[,1],fpdas3$label)
Silh(fpdas3$probability)
summary(fpdas3)
plot(fpdas3)
# Asymmetric data set clustering example (with shape 20).
data('asymmetric20')
x<-asymmetric20[,-1]
#Clustering
fpdas20=FPDC(x,4,3,3)
#Results
table(asymmetric20[,1],fpdas20$label)
Silh(fpdas20$probability)
summary(fpdas20)
plot(fpdas20)
# Clustering example with outliers.
data('outliers')
x<-outliers[,-1]
#Clustering
fpdout=FPDC(x,4,5,4)
#Results
table(outliers[,1],fpdout$label)
Silh(fpdout$probability)
summary(fpdout)
plot(fpdout)
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