doc/maskmeans.R

In andreamrau/maskmeans: Multi-view Aggregation and Splitting for K-Means Clustering

## ---- include=FALSE-----------------------------------------------------------
knitr::opts_chunk$set(dev='png')

## -----------------------------------------------------------------------------
library(fclust)
library(mclust)
library(maskmeans)
library(ggraph)
library(viridis)

## -----------------------------------------------------------------------------
set.seed(12345)
sim <- mv_simulate(beta=5, n=200, K=9, sigma=1.5)
X <- sim$data         ## Simulated data
mv <- c(2,2,2,1,1,2)  ## Size of each view
all.equal(sum(mv), ncol(X))

## ---- fig.width = 10, fig.height=6--------------------------------------------
mv_plot(mv_data=X, mv=mv, labels=sim$labels[,1])

## -----------------------------------------------------------------------------
aggregate_hard_init <- kmeans(X[,1:2], centers=15)$cluster
aggregate_soft_init <- FKM(X[,1:2], k=15, maxit=10, RS=10)$U
split_hard_init <- kmeans(X[,1:2], centers=2)$cluster
split_soft_init <- FKM(X[,1:2], k=2, maxit=10, RS=10)$U

## -----------------------------------------------------------------------------
hard_aggreg <- maskmeans(mv_data=X, mv=mv, clustering_init=aggregate_hard_init, 
                         type = "aggregation", gamma=2) 

## ---- fig.height = 8, fig.width=10--------------------------------------------
hard_aggreg$final_K
p <- maskmeans_plot(hard_aggreg)

## ---- fig.width = 10, fig.height=6--------------------------------------------
mv_plot(mv_data=X, mv=mv, labels=hard_aggreg$final_classification) +
  scale_color_viridis(discrete=TRUE) +
  scale_fill_viridis(discrete=TRUE)
adjustedRandIndex(hard_aggreg$final_classification, sim$labels[,1])
adjustedRandIndex(aggregate_hard_init, sim$labels[,1])

## -----------------------------------------------------------------------------
soft_aggreg <- maskmeans(mv_data=X, mv=mv, clustering_init=aggregate_soft_init, 
                         type = "aggregation", gamma=2) 

## ---- fig.height = 8, fig.width=10--------------------------------------------
soft_aggreg$final_K
p <- maskmeans_plot(soft_aggreg)

## ---- fig.width = 10, fig.height=6--------------------------------------------
# mv_plot(mv_data=X, mv=mv, labels=soft_aggreg$final_classification)
adjustedRandIndex(soft_aggreg$final_classification, sim$labels[,1])
adjustedRandIndex(apply(aggregate_soft_init, 1, which.max), sim$labels[,1])

## -----------------------------------------------------------------------------
hard_split <- maskmeans(mv_data=X, mv=mv, clustering_init=split_hard_init, Kmax=20,
                         type = "splitting", gamma=2, perCluster_mv_weights=FALSE) 

## ---- fig.height = 10, fig.width=10-------------------------------------------
hard_split$final_K
p <- maskmeans_plot(hard_split)

## ---- fig.width = 10, fig.height=6--------------------------------------------
mv_plot(mv_data=X, mv=mv, labels=hard_split$final_classification) +
  scale_color_viridis(discrete=TRUE) +
  scale_fill_viridis(discrete=TRUE)
adjustedRandIndex(hard_split$final_classification, sim$labels[,1])
adjustedRandIndex(split_hard_init, sim$labels[,1])

## -----------------------------------------------------------------------------
hard_split_perCluster <- maskmeans(mv_data=X, mv=mv, clustering_init=split_hard_init, Kmax=20,
                         type = "splitting", gamma=2, perCluster_mv_weights=TRUE) 

## ---- fig.height = 8, fig.width=10--------------------------------------------
hard_split_perCluster$final_K
p <- maskmeans_plot(hard_split_perCluster, type = c("criterion", "tree"))

## ---- fig.height = 8, fig.width=10--------------------------------------------
hard_split_perCluster$final_K
p <- maskmeans_plot(hard_split_perCluster, type = "tree_perClusterWeights")

## ----  fig.height=8, fig.width=4----------------------------------------------
p <- maskmeans_plot(hard_split_perCluster, type = c("weights"))

## ---- fig.width = 10, fig.height=6--------------------------------------------
mv_plot(mv_data=X, mv=mv, labels=hard_split_perCluster$final_classification) +
  scale_color_viridis(discrete=TRUE) +
  scale_fill_viridis(discrete=TRUE)
adjustedRandIndex(hard_split_perCluster$final_classification, sim$labels[,1])
adjustedRandIndex(split_hard_init, sim$labels[,1])

## ---- eval=FALSE--------------------------------------------------------------
#  Xlist <- list(X[,1:2], X[,3:4], X[,5:6], matrix(X[,7], ncol=1),
#                matrix(X[,8], ncol=1), X[,9:10])
#  hard_aggreg_list <- maskmeans(mv_data=Xlist,
#                                clustering_init=aggregate_hard_init,
#                                type = "aggregation", gamma=gamma)

## -----------------------------------------------------------------------------
sessionInfo()