Started playing around a bit with clustering

code/04_clustering_haum.R

@@ -0,0 +1,122 @@
+# 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)])
+