mtt_haum/code/04_clustering_haum.R

# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/code")

# Read data

dat0 <- read.table("../data/haum/event_logfiles_metadata_2023-09-23_01-31-30.csv",
                  sep = ";", header = TRUE)
dat0$date <- as.Date(dat0$date)
dat0$date.start <- as.POSIXct(dat0$date.start)
dat0$date.stop <- as.POSIXct(dat0$date.stop)
dat0$artwork <- sprintf("%03d", dat0$artwork)

# Preprocess variables for clustering

str(dat0)

# year            --> lubridate::year()
# duration        --> numeric, remove NA
# topicNumber     --> numeric, remove NA
# distance        --> numeric, remove NA
# scaleSize       --> numeric, remove NA
# rotationDegree  --> numeric, remove NA
# holiday         --> one/hot coding
# vacations       --> one/hot coding
# artwork?        --> one/hot coding (72 new variables)
# event?          --> one/hot coding  (4 new variables)

dat <- dat0

dat$year            <- lubridate::year(dat$date)
dat$holiday1        <- ifelse(is.na(dat$holiday), 0, 1)
dat$vacations1      <- ifelse(is.na(dat$vacations), 0, 1)
dat$topicNumber1    <- ifelse(is.na(dat$topicNumber), 0, dat$topicNumber)
dat$duration1       <- ifelse(is.na(dat$duration), 0, dat$duration)
dat$distance1       <- ifelse(is.na(dat$distance), 0, dat$distance)
dat$scaleSize1      <- ifelse(is.na(dat$scaleSize), 0, dat$scaleSize)
dat$rotationDegree1 <- ifelse(is.na(dat$rotationDegree), 0, dat$rotationDegree)

for (artwork in unique(dat$artwork)) {
  dat[[paste0("A", artwork)]] <- ifelse(dat$artwork == artwork, 1, 0)
}

for (event in unique(dat$event)) {
  dat[[event]] <- ifelse(dat$event == event, 1, 0)
}

mat <- dat[, c("year", "duration", "topicNumber", "distance", "scaleSize",
               "rotationDegree", "holiday1", "vacations1",
               paste0("A", unique(dat$artwork)), "flipCard", "move", "openTopic",
               "openPopup")]


mat1 <- dat[, c("year", "duration1", "topicNumber1", "distance1", "scaleSize1",
               "rotationDegree1", "holiday1", "vacations1",
               paste0("A", unique(dat$artwork)), "flipCard", "move", "openTopic",
               "openPopup")]

library(cluster)  # for hiereachical clustering

k1 <- kmeans(mat1, 2)
dat$kcluster <- k1$cluster

mat1$artwork <- dat$artwork
datagg <- aggregate(. ~ artwork, mat1, mean)
aa <- datagg$artwork
datagg$artwork <- NULL

k2 <- kmeans(datagg, 3)
datagg$cluster <- k2$cluster
datagg <- datagg[order(datagg$cluster), ]
aggregate(cbind(duration1, distance1, scaleSize1, rotationDegree1,
                holiday1, vacations1) ~ cluster, datagg, mean)
# --> how to interpret this??


# sample data for hierarchical clustering
n <- 200
set.seed(1826)

mat2 <- mat1[sample(nrow(mat1), n), ]
rownames(mat2) <- NULL
a1 <- agnes(mat2)

d1 <- as.dendrogram(a1)
plot(d1)

datagg$cluster <- NULL
rownames(datagg) <- NULL
a2 <- agnes(datagg)
d2 <- as.dendrogram(a2)
plot(d2)

## Clustering for nominal features with nomclust package

library(nomclust)

dat <- as.data.frame(lapply(dat0[, c("folder", "holiday", "vacations", "artwork",
                       "event", "case", "trace")], as.factor))
mat           <- list()
mat$year      <- as.numeric(dat$folder)
mat$holiday   <- as.numeric(dat$holiday)
mat$vacations <- as.numeric(dat$vacations)
mat$artwork   <- as.numeric(dat$artwork)
mat$event     <- as.numeric(dat$event)
mat$case      <- as.numeric(dat$case)
mat$trace     <- as.numeric(dat$trace)

mat$holiday   <- ifelse(is.na(mat$holiday), 0, 1)
mat$vacations <- ifelse(is.na(mat$vacations), 0, 1)

set.seed(1526)
ids <- sample(nrow(mat), 1000)
mat_small <- mat[ids, ] 

n1 <- nomclust(mat_small)

n1$mem$clu_3
dend.plot(n1, clusters = 3)

mat_small[n1$mem$clu_6 == 6, ]

cbind(mat_small[order(n1$mem$clu_3), ], n1$mem$clu_3[order(n1$mem$clu_3)])