Re-ran preprocessing and updated files; worked on user navigation behavior -- intermediate step
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@ -126,6 +126,8 @@ dat1 <- merge(datlogs, hd, by.x = "date", by.y = "date", all.x = TRUE)
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dat2 <- merge(dat1, sfdat, by.x = "date", by.y = "date", all.x = TRUE)
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dat2$date <- NULL
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dat2 <- dat2[order(dat2$fileId.start, dat2$date.start, dat2$timeMs.start), ]
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## Export data
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@ -9,7 +9,7 @@
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# (3.4) Artwork sequences
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# (3.5) Topics
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#
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# input: results/haum/event_logfiles_glossar_2023-12-28_09-49-43.csv
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# input: results/haum/event_logfiles_2024-02-21_16-07-33.csv
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# output:
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#
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# last mod: 2023-11-15, NW
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@ -27,7 +27,7 @@ library(bupaverse)
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#--------------- (1) Read data ---------------
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datlogs <- read.table("results/haum/event_logfiles_2024-01-18_09-58-52.csv",
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datlogs <- read.table("results/haum/event_logfiles_2024-02-21_16-07-33.csv",
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colClasses = c("character", "character", "POSIXct",
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"POSIXct", "character", "integer",
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"numeric", "character", "character",
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@ -5,7 +5,7 @@ from python_helpers import eval_pm, pn_infos_miner
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###### Load data and create event logs ######
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dat = pd.read_csv("results/haum/event_logfiles_2024-01-18_09-58-52.csv", sep = ";")
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dat = pd.read_csv("results/haum/event_logfiles_2024-02-21_16-07-33.csv", sep = ";")
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event_log = pm4py.format_dataframe(dat, case_id = "path",
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activity_key = "event",
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@ -53,6 +53,7 @@ for i in range(len(replayed_traces)):
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set(l1)
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x1 = np.array(l1)
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index_broken = np.where(x1 == 1)[0].tolist()
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len(index_broken)
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set(l3)
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l4.count([])
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@ -2,11 +2,11 @@
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#--------------- (1) Look at broken trace ---------------
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datraw <- read.table("results/haum/raw_logfiles_2024-01-18_09-58-52.csv",
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datraw <- read.table("results/haum/raw_logfiles_2024-02-21_16-07-33.csv",
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header = TRUE, sep = ";")
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datlogs <- read.table("results/haum/event_logfiles_2024-01-18_09-58-52.csv",
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datlogs <- read.table("results/haum/event_logfiles_2024-02-21_16-07-33.csv",
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colClasses = c("character", "character", "POSIXct",
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"POSIXct", "character", "integer",
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"numeric", "character", "character",
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@ -6,7 +6,7 @@ from python_helpers import eval_pm, pn_infos
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###### Load data and create event logs ######
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dat = pd.read_csv("results/haum/event_logfiles_2024-01-18_09-58-52.csv", sep = ";")
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dat = pd.read_csv("results/haum/event_logfiles_2024-02-21_16-07-33.csv", sep = ";")
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dat = dat[dat["date.start"] < "2020-03-13"]
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# --> only pre corona (before artworks were updated)
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dat = dat[dat["path"] != 106098]
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@ -1,4 +1,4 @@
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# 08_item-clustering.R
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# 07_item-clustering.R
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#
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# content: (1) Read data
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# (1.1) Read log event data
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@ -7,11 +7,11 @@
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# (2) Clustering
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# (3) Visualization with pictures
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#
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# input: results/haum/event_logfiles_2024-01-18_09-58-52.csv
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# input: results/haum/event_logfiles_2024-02-21_16-07-33.csv
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# results/haum/pn_infos_items.csv
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# output: results/haum/event_logfiles_pre-corona_with-clusters.csv
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#
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# last mod: 2024-01-30
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# last mod: 2024-02-23
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# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code")
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@ -23,7 +23,7 @@ library(factoextra)
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#--------------- (1.1) Read log event data ---------------
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dat0 <- read.table("results/haum/event_logfiles_2024-01-18_09-58-52.csv",
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dat0 <- read.table("results/haum/event_logfiles_2024-02-21_16-07-33.csv",
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colClasses = c("character", "character", "POSIXct",
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"POSIXct", "character", "integer",
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"numeric", "character", "character",
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@ -48,21 +48,47 @@ datitem <- read.table("results/haum/pn_infos_items.csv", header = TRUE,
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#--------------- (1.3) Extract additional infos for clustering ---------------
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datitem$duration <- aggregate(duration ~ item, dat, mean)$duration
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dat_split <- split(dat, ~ path)
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time_minmax <- function(subdata) {
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subdata$min_time <- min(subdata$timeMs.start)
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if (all(is.na(subdata$timeMs.stop))) {
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subdata$max_time <- NA
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} else {
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subdata$max_time <- max(subdata$timeMs.stop, na.rm = TRUE)
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}
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subdata
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}
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dat_list <- pbapply::pblapply(dat_split, time_minmax)
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dat_minmax <- dplyr::bind_rows(dat_list)
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datpath <- aggregate(duration ~ item + path, dat, mean, na.action = NULL)
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datpath$min_time <- aggregate(min_time ~ path, dat_minmax, unique, na.action = NULL)$min_time
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datpath$max_time <- aggregate(max_time ~ path, dat_minmax, unique, na.action = NULL)$max_time
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datpath$duration_path <- datpath$max_time - datpath$min_time
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# average duration per path
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datitem$duration <- aggregate(duration ~ item, datpath, mean)$duration
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datitem$distance <- aggregate(distance ~ item, dat, mean)$distance
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datitem$scaleSize <- aggregate(scaleSize ~ item, dat, mean)$scaleSize
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datitem$rotationDegree <- aggregate(rotationDegree ~ item, dat, mean)$rotationDegree
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datitem$npaths <- aggregate(path ~ item, dat, function(x) length(unique(x)))$path
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datitem$ncases <- aggregate(case ~ item, dat, function(x) length(unique(x)))$case
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datitem$ntopics <- aggregate(topic ~ item, dat, function(x) length(unique(x)))$topic
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datitem$mostfreq_num <- as.numeric(gsub(".*: (.*)}", "\\1", datitem$mostfreq))
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datitem$nmoves <- aggregate(event ~ item, dat, table)$event[,"move"]
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datitem$nflipCard <- aggregate(event ~ item, dat, table)$event[,"flipCard"]
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datitem$nopenTopic <- aggregate(event ~ item, dat, table)$event[,"openTopic"]
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datitem$nopenPopup <- aggregate(event ~ item, dat, table)$event[,"openPopup"]
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#--------------- (2) Clustering ---------------
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df <- datitem[, c("precision", "generalizability", "nvariants",
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"mostfreq_num", "duration", "distance", "scaleSize",
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"rotationDegree", "npaths", "ncases", "ntopics")] |>
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scale()
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df <- datitem[, c("precision", "generalizability", "nvariants", "duration",
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"distance", "scaleSize", "rotationDegree", "npaths",
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"ncases", "nmoves", "nopenTopic", "nopenPopup")] |>
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scale()
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mat <- dist(df)
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heatmap(as.matrix(mat))
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@ -88,6 +114,7 @@ cor(mat, c3)
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cor(mat, c4)
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cor(mat, c5)
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# https://en.wikipedia.org/wiki/Cophenetic_correlation
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# https://stats.stackexchange.com/questions/195446/choosing-the-right-linkage-method-for-hierarchical-clustering
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# Dendograms
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par(mfrow=c(3,2))
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@ -101,15 +128,15 @@ plot(h5, main = "Single Linkage")
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hc <- h1
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# Note that ‘agnes(*, method="ward")’ corresponds to ‘hclust(*, "ward.D2")’
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k <- 7 # number of clusters
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k <- 4 # number of clusters
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grp <- cutree(hc, k = k)
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datitem$grp <- grp
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fviz_dend(hc, k = k,
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cex = 0.5,
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k_colors = c("#78004B", "#000000", "#3CB4DC", "#91C86E",
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"#FF6900", "gold", "#434F4F"),
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k_colors = c("#78004B", "#FF6900", "#3CB4DC", "#91C86E",
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"#000000", "gold", "#434F4F"),
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#type = "phylogenic",
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rect = TRUE
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)
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@ -120,15 +147,16 @@ rect.hclust(hc, k=7, border="blue")
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rect.hclust(hc, k=6, border="green")
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p <- fviz_cluster(list(data = df, cluster = grp),
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palette = c("#78004B", "#000000", "#3CB4DC", "#91C86E",
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"#FF6900", "#434F4F", "gold"),
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palette = c("#78004B", "#FF6900", "#3CB4DC", "#91C86E",
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"#000000", "#434F4F", "gold"),
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ellipse.type = "convex",
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repel = TRUE,
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show.clust.cent = FALSE, ggtheme = theme_bw())
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p
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aggregate(cbind(duration, distance, scaleSize , rotationDegree, npaths,
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ncases, ntopics) ~ grp, datitem, median)
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ncases, nmoves, nflipCard, nopenTopic, nopenPopup) ~ grp,
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datitem, median)
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# Something like a scree plot (??)
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plot(rev(seq_along(hc$height)), hc$height, type = "l")
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@ -189,15 +217,15 @@ library(png)
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library(jpeg)
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library(grid)
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colors <- c("#78004B", "#000000", "#3CB4DC", "#91C86E", "#FF6900",
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"#434F4F")
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colors <- c("#78004B", "#FF6900", "#3CB4DC", "#91C86E")
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#pdf("results/haum/figures/clustering_artworks.pdf", height = 8, width = 8, pointsize = 10)
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png("results/haum/figures/clustering_artworks.png", units = "in", height = 8, width = 8, pointsize = 10, res = 300)
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pdf("results/figures/clustering_artworks.pdf", height = 8, width = 8, pointsize = 10)
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#png("results/figures/clustering_artworks.png", units = "in", height = 8, width = 8, pointsize = 10, res = 300)
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par(mai = c(.6,.6,.1,.1), mgp = c(2.4, 1, 0))
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par(mai = c(.4,.4,.1,.1), mgp = c(2.4, 1, 0))
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plot(y ~ x, p$data, type = "n", ylim = c(-3.2, 3), xlim = c(-4.7, 6.4))
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plot(y ~ x, p$data, type = "n", ylim = c(-3.5, 2.8), xlim = c(-5, 10),
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xlab = "", ylab = "")
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for (item in sprintf("%03d", as.numeric(rownames(p$data)))) {
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@ -221,12 +249,13 @@ for (item in sprintf("%03d", as.numeric(rownames(p$data)))) {
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pch = 15)
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rasterImage(img,
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xleft = x - .4,
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xright = x + .4,
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xleft = x - .45,
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xright = x + .45,
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ybottom = y - .2,
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ytop = y + .2)
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}
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legend("topright", paste("Cluster", 1:k), col = colors, pch = 15, bty = "n")
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dev.off()
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@ -6,10 +6,11 @@
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# (2) Clustering
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# (3) Investigate variants
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#
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# input: results/haum/event_logfiles_2024-01-18_09-58-52.csv
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# input: results/haum/event_logfiles_2024-02-21_16-07-33.csv
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# output: results/haum/event_logfiles_pre-corona_with-clusters_cases.csv
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# results/haum/dattree.csv
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#
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# last mod: 2024-02-07
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# last mod: 2024-02-23
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# setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code")
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@ -21,17 +22,18 @@ library(factoextra)
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#--------------- (1.1) Read log event data ---------------
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dat0 <- read.table("results/haum/event_logfiles_2024-01-18_09-58-52.csv",
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colClasses = c("character", "character", "POSIXct",
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"POSIXct", "character", "integer",
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"numeric", "character", "character",
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rep("numeric", 3), "character",
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"character", rep("numeric", 11),
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"character", "character"),
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sep = ";", header = TRUE)
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dat0 <- read.table("results/haum/event_logfiles_2024-02-21_16-07-33.csv",
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colClasses = c("character", "character", "POSIXct",
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"POSIXct", "character", "integer",
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"numeric", "character", "character",
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rep("numeric", 3), "character",
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"character", rep("numeric", 11),
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"character", "character"),
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sep = ";", header = TRUE)
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dat0$event <- factor(dat0$event, levels = c("move", "flipCard", "openTopic",
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"openPopup"))
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dat0$topic <- factor(dat0$topic)
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dat0$weekdays <- factor(weekdays(dat0$date.start),
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levels = c("Montag", "Dienstag", "Mittwoch",
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@ -48,18 +50,43 @@ dat <- dat[dat$path != 106098, ]
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#--------------- (1.2) Extract additional infos for clustering ---------------
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datcase <- aggregate(cbind(duration, distance, scaleSize, rotationDegree) ~
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datcase <- aggregate(cbind(distance, scaleSize, rotationDegree) ~
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case, dat, function(x) mean(x, na.rm = TRUE), na.action = NULL)
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datcase$length <- aggregate(item ~ case, dat, length)$item
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eventtab <- aggregate(event ~ case, dat, table)["case"]
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eventtab$nmove <- aggregate(event ~ case, dat, table)$event[, "move"]
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eventtab$nflipCard <- aggregate(event ~ case, dat, table)$event[, "flipCard"]
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eventtab$nopenTopic <- aggregate(event ~ case, dat, table)$event[, "openTopic"]
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eventtab$nopenPopup <- aggregate(event ~ case, dat, table)$event[, "openPopup"]
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topictab <- aggregate(topic ~ case, dat, table)["case"]
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topictab$artist <- aggregate(topic ~ case, dat, table)$topic[, 1]
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topictab$details <- aggregate(topic ~ case, dat, table)$topic[, 2]
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topictab$extra_info <- aggregate(topic ~ case, dat, table)$topic[, 3]
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topictab$komposition <- aggregate(topic ~ case, dat, table)$topic[, 4]
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topictab$leben_des_kunstwerks <- aggregate(topic ~ case, dat, table)$topic[, 5]
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topictab$licht_und_farbe <- aggregate(topic ~ case, dat, table)$topic[, 6]
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topictab$technik <- aggregate(topic ~ case, dat, table)$topic[, 7]
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topictab$thema <- aggregate(topic ~ case, dat, table)$topic[, 8]
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datcase <- datcase |>
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merge(eventtab, by = "case", all = TRUE) |>
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merge(topictab, by = "case", all = TRUE)
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datcase$ntopiccards <- aggregate(topic ~ case, dat,
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function(x) ifelse(all(is.na(x)), NA,
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length(na.omit(x))), na.action =
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NULL)$topic
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datcase$ntopics <- aggregate(topic ~ case, dat,
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function(x) ifelse(all(is.na(x)), NA,
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length(unique(na.omit(x)))), na.action =
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NULL)$topic
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datcase$nitems <- aggregate(item ~ case, dat, function(x)
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length(unique(x)), na.action = NULL)$item
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datcase$npaths <- aggregate(path ~ case, dat, function(x)
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length(unique(x)), na.action = NULL)$path
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# datcase$ntopics <- aggregate(topic ~ case, dat,
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# function(x) ifelse(all(is.na(x)), NA,
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# length(unique(na.omit(x)))), na.action =
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# NULL)$topic
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datcase$vacation <- aggregate(vacation ~ case, dat,
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function(x) ifelse(all(is.na(x)), 0, 1),
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na.action = NULL)$vacation
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@ -73,42 +100,370 @@ datcase$morning <- aggregate(date.start ~ case, dat,
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function(x) ifelse(lubridate::hour(x[1]) > 13, 0, 1),
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na.action = NULL)$date.start
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datcase <- na.omit(datcase)
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dat_split <- split(dat, ~ case)
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#--------------- (2) Clustering ---------------
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time_minmax <- function(subdata) {
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subdata$min_time <- min(subdata$timeMs.start)
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if (all(is.na(subdata$timeMs.stop))) {
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subdata$max_time <- NA
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} else {
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subdata$max_time <- max(subdata$timeMs.stop, na.rm = TRUE)
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}
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subdata
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}
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dat_list <- pbapply::pblapply(dat_split, time_minmax)
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dat_minmax <- dplyr::bind_rows(dat_list)
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datcase$min_time <- aggregate(min_time ~ case, dat_minmax, unique)$min_time
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datcase$max_time <- aggregate(max_time ~ case, dat_minmax, unique)$max_time
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datcase$duration <- datcase$max_time - datcase$min_time
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datcase$min_time <- NULL
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datcase$max_time <- NULL
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cor_mat <- cor(datcase[, -1], use = "pairwise")
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diag(cor_mat) <- NA
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heatmap(cor_mat)
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# TODO: Add info if all items of a case are information cards??
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# Navigation types by Bousbia et al. (2010):
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# - Overviewing: this value is close to the Canter “scanning” value. It
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# implies that the learner is covering a large proportion of course pages.
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# Through this phase of fast-reading, the user seeks to acquire an
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# overall view of the course.
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# - Flitting: close to “wandering”. It reflects a browsing activity without a
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# strategy or a particular goal. The main difference with the overviewing
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# type is the lack of focus on the course.
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# - Studying: corresponds to a partial or complete reading of the course
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# pages where the learner spends time on each page.
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# - Deepening: This describes a learner who spends relatively long time on a
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# course, checking details, and seeking Web documents related to the course
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# topics. The main difference with studying is the Web search part that the
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# learner uses to obtain a deeper understanding of the course.
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# Taxonomy defined by Canter et al. (1985):
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# - Scanning: seeking an overview of a theme (i.e. subpart of the hypermedia)
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# by requesting an important proportion of its pages but without spending
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# much time on them.
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# - Browsing: going wherever the data leads the navigator until catching an
|
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# interest.
|
||||
# - Exploring: reading the viewed pages thoroughly.
|
||||
# - Searching: seeking for a particular document or information.
|
||||
# - Wandering: navigating in an unstructured fashion without any particular
|
||||
# goal or strategy.
|
||||
|
||||
# Features for navigation types for MTT:
|
||||
# - Scanning / Overviewing:
|
||||
# * Proportion of artworks looked at is high: datcase$nitems / 70
|
||||
# * Duration per artwork is low: "ave_duration_item" / datcase$duration
|
||||
# - Exploring:
|
||||
# * Looking at additional information for most items touched (high value):
|
||||
# harmonic mean of datcase$nopenTopic / datcase$nflipCard and
|
||||
# datcase$nopenPopup / datcase$nopenTopic
|
||||
# - Searching / Studying:
|
||||
# * Looking only at a few items
|
||||
# datcase$nitems / 70 is low
|
||||
# * Opening few cards
|
||||
# datcase$nflipCard / mean(datcase$nflipCard) or median(datcase$nflipCard) is low
|
||||
# * but for most cards popups are opened:
|
||||
# datcase$nopenPopup / datcase$nflipCard is high
|
||||
# - Wandering / Flitting:
|
||||
# * Items are mostly just moved:
|
||||
# datcase$nmove / datcase$length is high
|
||||
# * Duration per case is low:
|
||||
# datcase$duration / mean(datcase$duration) or median(datcase$duration)
|
||||
# * Duration per artwork is low: "ave_duration_item" / datcase$duration
|
||||
|
||||
# TODO: Come up with relevant features for navigation behavior
|
||||
|
||||
|
||||
|
||||
dattree <- data.frame(case = datcase$case,
|
||||
Duration = datcase$duration,
|
||||
PropItems = datcase$nitems / length(unique(dat$item)),
|
||||
SearchInfo =
|
||||
2*(((datcase$nopenPopup / datcase$nopenTopic) *
|
||||
(datcase$nopenTopic / datcase$nflipCard)) /
|
||||
((datcase$nopenPopup / datcase$nopenTopic) +
|
||||
(datcase$nopenTopic / datcase$nflipCard))
|
||||
),
|
||||
PropMoves = datcase$nmove / datcase$length,
|
||||
PathLinearity = datcase$nitems / datcase$npaths,
|
||||
Singularity = datcase$npaths / datcase$length
|
||||
)
|
||||
|
||||
dattree$SearchInfo <- ifelse(dattree$SearchInfo %in% 0, 0.1, dattree$SearchInfo)
|
||||
dattree$SearchInfo <- ifelse(is.na(dattree$SearchInfo), 0, dattree$SearchInfo)
|
||||
|
||||
get_centrality <- function(case, data) {
|
||||
|
||||
data$start <- data$date.start
|
||||
data$complete <- data$date.stop
|
||||
|
||||
alog <- activitylog(data[data$case == case, ],
|
||||
case_id = "case",
|
||||
activity_id = "item",
|
||||
resource_id = "path",
|
||||
timestamps = c("start", "complete"))
|
||||
|
||||
net <- process_map(alog, render = FALSE)
|
||||
inet <- DiagrammeR::to_igraph(net)
|
||||
|
||||
c(igraph::centr_degree(inet, loops = FALSE)$centralization,
|
||||
igraph::centr_degree(inet, loops = TRUE)$centralization,
|
||||
igraph::centr_betw(inet)$centralization)
|
||||
}
|
||||
|
||||
|
||||
centrality <- lapply(dattree$case, get_centrality, data = dat)
|
||||
centrality <- do.call(rbind, centrality)
|
||||
|
||||
save(centrality, file = "results/haum/tmp_centrality.RData")
|
||||
|
||||
dattree$centr_degree <- centrality[, 1]
|
||||
dattree$centr_degree_loops <- centrality[, 2]
|
||||
dattree$centr_between <- centrality[, 3]
|
||||
|
||||
par(mfrow = c(3,3))
|
||||
hist(dattree$Duration, breaks = 50, main = "")
|
||||
hist(dattree$SearchInfo, breaks = 50, main = "")
|
||||
hist(dattree$PropItems, breaks = 50, main = "")
|
||||
hist(dattree$PropMoves, breaks = 50, main = "")
|
||||
hist(dattree$PathLinearity, breaks = 50, main = "")
|
||||
hist(dattree$Singularity, breaks = 50, main = "")
|
||||
hist(dattree$centr_degree, breaks = 50, main = "")
|
||||
hist(dattree$centr_degree_loops, breaks = 50, main = "")
|
||||
hist(dattree$centr_between, breaks = 50, main = "")
|
||||
|
||||
|
||||
cor_mat <- cor(dattree[, -1], use = "pairwise")
|
||||
diag(cor_mat) <- NA
|
||||
heatmap(cor_mat)
|
||||
|
||||
|
||||
dattree$Pattern <- "Dispersion"
|
||||
dattree$Pattern <- ifelse(dattree$PathLinearity > 0.8 &
|
||||
dattree$Singularity > 0.8, "Scholar",
|
||||
dattree$Pattern)
|
||||
dattree$Pattern <- ifelse(dattree$PathLinearity <= 0.8 &
|
||||
dattree$centr_between > 0.5, "Star",
|
||||
dattree$Pattern)
|
||||
|
||||
write.table(dattree,
|
||||
file = "results/haum/dattree.csv",
|
||||
sep = ";",
|
||||
quote = FALSE,
|
||||
row.names = FALSE)
|
||||
|
||||
|
||||
tmp <- dat
|
||||
tmp$start <- tmp$date.start
|
||||
tmp$complete <- tmp$date.stop
|
||||
|
||||
|
||||
alog <- activitylog(tmp[tmp$case == 3448, ],
|
||||
case_id = "case",
|
||||
activity_id = "item",
|
||||
resource_id = "path",
|
||||
timestamps = c("start", "complete"))
|
||||
|
||||
process_map(alog)
|
||||
|
||||
net <- process_map(alog, render = FALSE)
|
||||
#DiagrammeR::get_node_df(net)
|
||||
|
||||
DiagrammeR::get_node_info(net)
|
||||
|
||||
DiagrammeR::get_degree_distribution(net)
|
||||
|
||||
DiagrammeR::get_degree_in(net)
|
||||
DiagrammeR::get_degree_out(net)
|
||||
DiagrammeR::get_degree_total(net)
|
||||
|
||||
|
||||
N <- DiagrammeR::count_nodes(net) - 2 # Do not count start and stop nodes
|
||||
|
||||
dc <- DiagrammeR::get_degree_total(net)[1:N, "total_degree"] / (N - 1)
|
||||
|
||||
inet <- DiagrammeR::to_igraph(net)
|
||||
igraph::centr_degree(inet, loops = FALSE)
|
||||
igraph::centr_betw(inet)
|
||||
igraph::centr_clo(inet)
|
||||
|
||||
df <- dattree[, c("Duration", "PropItems", "SearchInfo", "PropMoves")]
|
||||
df$Scholar <- ifelse(dattree$Pattern == "Scholar", 1, 0)
|
||||
df$Star <- ifelse(dattree$Pattern == "Star", 1, 0)
|
||||
df$Dispersion <- ifelse(dattree$Pattern == "Dispersion", 1, 0)
|
||||
# scale Duration and min/max SearchInfo
|
||||
df$Duration <- scale(df$Duration)
|
||||
df$SearchInfo <- (df$SearchInfo - min(df$SearchInfo)) /
|
||||
(max(df$SearchInfo) - min(df$SearchInfo))
|
||||
|
||||
df <- datcase[, c("duration", "distance", "scaleSize", "rotationDegree",
|
||||
"length", "nitems", "npaths")] |>
|
||||
scale()
|
||||
#df <- cbind(df, datcase[, c("vacation", "holiday", "weekend", "morning")])
|
||||
mat <- dist(df)
|
||||
# TODO: Do I need to scale all variables?
|
||||
|
||||
hc <- hclust(mat, method = "ward.D2")
|
||||
h1 <- hclust(mat, method = "average")
|
||||
h2 <- hclust(mat, method = "complete")
|
||||
h3 <- hclust(mat, method = "ward.D")
|
||||
h4 <- hclust(mat, method = "ward.D2")
|
||||
h5 <- hclust(mat, method = "single")
|
||||
|
||||
grp <- cutree(hc, k = 6)
|
||||
datcase$grp <- grp
|
||||
# Cophenetic Distances, for each linkage (runs quite some time!)
|
||||
c1 <- cophenetic(h1)
|
||||
c2 <- cophenetic(h2)
|
||||
c3 <- cophenetic(h3)
|
||||
c4 <- cophenetic(h4)
|
||||
c5 <- cophenetic(h5)
|
||||
|
||||
# Correlations
|
||||
cor(mat, c1)
|
||||
# 0.9029232
|
||||
cor(mat, c2)
|
||||
# 0.8879478
|
||||
cor(mat, c3)
|
||||
# 0.5747296
|
||||
cor(mat, c4)
|
||||
# 0.5994121
|
||||
cor(mat, c5)
|
||||
# 0.5292353
|
||||
|
||||
# https://en.wikipedia.org/wiki/Cophenetic_correlation
|
||||
# https://stats.stackexchange.com/questions/195446/choosing-the-right-linkage-method-for-hierarchical-clustering
|
||||
|
||||
hc <- h1
|
||||
|
||||
# Something like a scree plot (??)
|
||||
plot(rev(hc$height)[1:100], type = "b", pch = 16, cex = .5)
|
||||
|
||||
k <- 4
|
||||
|
||||
grp <- cutree(hc, k = k)
|
||||
df$grp <- grp
|
||||
|
||||
table(grp)
|
||||
|
||||
fviz_cluster(list(data = df, cluster = grp),
|
||||
palette = c("#78004B", "#000000", "#3CB4DC", "#91C86E",
|
||||
"#FF6900", "#434F4F"),
|
||||
#palette = c("#78004B", "#FF6900", "#3CB4DC", "#91C86E",
|
||||
# "#000000", "#434F4F"),
|
||||
ellipse.type = "convex",
|
||||
show.clust.cent = FALSE, ggtheme = theme_bw())
|
||||
|
||||
aggregate(cbind(duration, distance, scaleSize , rotationDegree, length,
|
||||
nitems, npaths) ~ grp, datcase, mean)
|
||||
aggregate(cbind(duration, distance, scaleSize , rotationDegree, length,
|
||||
nitems, npaths) ~ grp, datcase, median)
|
||||
aggregate(cbind(duration, distance, scaleSize , rotationDegree, length,
|
||||
nitems, npaths) ~ grp, datcase, max)
|
||||
table(dattree[dattree$grp == 3, "Pattern"])
|
||||
|
||||
res <- merge(dat, datcase[, c("case", "grp")], by = "case", all.x = TRUE)
|
||||
|
||||
aggregate(cbind(Duration, PropItems, SearchInfo, PropMoves, PathLinearity,
|
||||
Singularity, centr_degree, centr_degree_loops,
|
||||
centr_between) ~ grp, dattree, mean)
|
||||
|
||||
|
||||
aggregate(cbind(Duration, PropItems, SearchInfo, PropMoves, Dispersion,
|
||||
Scholar, Star) ~ grp, df, mean)
|
||||
|
||||
|
||||
# "We first extract the graph sub-sequences corresponding to the four
|
||||
# patterns of Canter et al. (1985). We also identified the number of nodes
|
||||
# to which the learner often goes back to (Fig. 4). These nodes are called
|
||||
# “central nodes”. If the number of central nodes is lower than or equal to
|
||||
# half of the sub-sequences, the browsing pattern indicator takes on the
|
||||
# value “Star”." Bousbia et al. (2010)
|
||||
|
||||
# I do not know how they got the sub-sequences. I am taking the ratio of
|
||||
# strongly connected nodes to weakly connected nodes. If the number of
|
||||
# weakly connected nodes is twice as high, the pattern is classified as a
|
||||
# star, i.e., NodeConnect <= 0.5.
|
||||
# TODO: This does not make sense, smallest and most frequent number is 3!
|
||||
# (and I do not understand it...)
|
||||
|
||||
|
||||
# count_asymmetric_node_pairs Get the number of asymmetrically-connected node pairs
|
||||
# count_edges Get a count of all edges
|
||||
# count_loop_edges Get count of all loop edges
|
||||
# count_mutual_node_pairs Get the number of mutually-connected node pairs
|
||||
# count_unconnected_node_pairs Get the number of unconnected node pairs
|
||||
# count_unconnected_nodes Get count of all unconnected nodes
|
||||
|
||||
|
||||
# TODO: Read up on centrality measures
|
||||
# https://www.r-bloggers.com/2018/12/network-centrality-in-r-an-introduction/
|
||||
# https://www.datacamp.com/tutorial/centrality-network-analysis-R
|
||||
# http://davidrajuh.net/reggie/publications/publications-filer/rd114-2018-Network-Centrality.pdf
|
||||
# https://link.springer.com/article/10.1007/s10618-024-01003-4
|
||||
|
||||
#--------------- (2) Clustering ---------------
|
||||
|
||||
df <- na.omit(datcase[, c("duration", "distance", "scaleSize",
|
||||
"rotationDegree", "length", "nmove",
|
||||
"nitems", "npaths")])
|
||||
|
||||
#df <- cbind(df, datcase[, c("vacation", "holiday", "weekend", "morning")])
|
||||
mat <- dist(scale(df))
|
||||
#mat <- dist(df)
|
||||
|
||||
h1 <- hclust(mat, method = "average")
|
||||
h2 <- hclust(mat, method = "complete")
|
||||
h3 <- hclust(mat, method = "ward.D")
|
||||
h4 <- hclust(mat, method = "ward.D2")
|
||||
h5 <- hclust(mat, method = "single")
|
||||
|
||||
# Cophenetic Distances, for each linkage (runs quite some time!)
|
||||
c1 <- cophenetic(h1)
|
||||
c2 <- cophenetic(h2)
|
||||
c3 <- cophenetic(h3)
|
||||
c4 <- cophenetic(h4)
|
||||
c5 <- cophenetic(h5)
|
||||
|
||||
# Correlations
|
||||
cor(mat, c1)
|
||||
cor(mat, c2)
|
||||
cor(mat, c3)
|
||||
cor(mat, c4)
|
||||
cor(mat, c5)
|
||||
# https://en.wikipedia.org/wiki/Cophenetic_correlation
|
||||
# https://stats.stackexchange.com/questions/195446/choosing-the-right-linkage-method-for-hierarchical-clustering
|
||||
|
||||
hc <- h1
|
||||
|
||||
# Something like a scree plot (??)
|
||||
plot(rev(hc$height)[1:100], type = "b", pch = 16, cex = .5)
|
||||
|
||||
|
||||
# TODO: Something is wrong
|
||||
|
||||
k <- 4
|
||||
|
||||
grp <- cutree(hc, k = k)
|
||||
df$grp <- grp
|
||||
|
||||
table(grp)
|
||||
|
||||
fviz_cluster(list(data = df, cluster = grp),
|
||||
#palette = c("#78004B", "#FF6900", "#3CB4DC", "#91C86E",
|
||||
# "#000000", "#434F4F"),
|
||||
ellipse.type = "convex",
|
||||
show.clust.cent = FALSE, ggtheme = theme_bw())
|
||||
|
||||
aggregate(cbind(duration, distance, scaleSize , rotationDegree, length, nmove,
|
||||
nitems, npaths) ~ grp, df, mean)
|
||||
aggregate(cbind(duration, distance, scaleSize , rotationDegree, length, nmove,
|
||||
nitems, npaths) ~ grp, df, median)
|
||||
aggregate(cbind(duration, distance, scaleSize , rotationDegree, length, nmove,
|
||||
nitems, npaths) ~ grp, df, max)
|
||||
|
||||
|
||||
df$case <- na.omit(datcase[, c("case", "duration", "distance", "scaleSize",
|
||||
"rotationDegree", "length", "nmove",
|
||||
"nitems", "npaths")])$case
|
||||
|
||||
res <- merge(dat, df[, c("case", "grp")], by = "case", all.x = TRUE)
|
||||
res <- res[order(res$fileId.start, res$date.start, res$timeMs.start), ]
|
||||
|
||||
xtabs( ~ item + grp, res)
|
||||
aggregate(event ~ grp, res, table)
|
||||
|
||||
# Look at clusters
|
||||
par(mfrow = c(2, 2))
|
||||
vioplot::vioplot(duration ~ grp, res)
|
||||
vioplot::vioplot(distance ~ grp, res)
|
||||
vioplot::vioplot(scaleSize ~ grp, res)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user