diff --git a/code/07_item-clustering.R b/code/07_item-clustering.R
index bd0e37a..e5a7ab8 100644
--- a/code/07_item-clustering.R
+++ b/code/07_item-clustering.R
@@ -10,12 +10,9 @@
 # input:  results/haum/eventlogs_pre-corona_cleaned.RData
 #         results/haum/pn_infos_items.csv
 # output: results/haum/eventlogs_pre-corona_item-clusters.csv
-#         results/figures/dendrogram_items.pdf
-#         results/figures/clustering_items.pdf
-#         results/figures/clustering_artworks.pdf
-#         results/figures/clustering_artworks.png
+#         ../../thesis/figures/data/clustering_items.RData"
 #
-# last mod: 2024-03-08
+# last mod: 2024-03-21
 
 # setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/analysis/code")
 
@@ -96,9 +93,6 @@ mycols <- c("#434F4F", "#78004B", "#FF6900", "#3CB4DC", "#91C86E", "Black")
 
 cluster <- cutree(hc, k = k)
 
-pdf("results/figures/dendrogram_items.pdf", width = 6.5, height = 5.5, pointsize = 10)
-# TODO: Move code for plots to /thesis/
-
 factoextra::fviz_dend(hc, k = k,
                       cex = 0.5,
                       k_colors = mycols,
@@ -109,10 +103,6 @@ factoextra::fviz_dend(hc, k = k,
                       #ggtheme = ggplot2::theme_bw()
 )
 
-dev.off()
-
-pdf("results/figures/clustering_items.pdf", width = 6.5, height = 5.5, pointsize = 10)
-
 factoextra::fviz_cluster(list(data = df, cluster = cluster),
                          palette = mycols,
                          ellipse.type = "convex",
@@ -121,8 +111,6 @@ factoextra::fviz_cluster(list(data = df, cluster = cluster),
                          main = "",
                          ggtheme = ggplot2::theme_bw())
 
-dev.off()
-
 aggregate(cbind(precision, generalizability, nvariants, duration, distance,
                 scaleSize , rotationDegree, npaths, ncases, nmoves,
                 nflipCard, nopenTopic, nopenPopup) ~ cluster, datitem,
@@ -151,75 +139,8 @@ write.table(res,
             quote = FALSE,
             row.names = FALSE)
 
-# DFGs for clusters
-res$start <- res$date.start
-res$complete <- res$date.stop
+# Save data for plots and tables
 
-for (clst in sort(unique(res$cluster))) {
-
-  alog <- bupaR::activitylog(res[res$cluster == clst, ],
-    case_id     = "path",
-    activity_id = "event",
-    resource_id = "item",
-    timestamps  = c("start", "complete"))
-  
-  dfg <- processmapR::process_map(alog,
-    type_nodes = processmapR::frequency("relative", color_scale = "Greys"),
-    sec_nodes  = processmapR::frequency("absolute"),
-    type_edges = processmapR::frequency("relative", color_edges = "#FF6900"),
-    sec_edges  = processmapR::frequency("absolute"),
-    rankdir    = "LR",
-    render     = FALSE)
-
-  processmapR::export_map(dfg,
-    file_name = paste0("results/processmaps/dfg_items_cluster", clst, "_R.pdf"),
-    file_type = "pdf",
-    title     = paste("Cluster", clst))
-}
-
-#--------------- (3) Visualization with pictures ---------------
-
-coor_2d <- cmdscale(dist_mat, k = 2)
-
-items <- sprintf("%03d", as.numeric(rownames(datitem)))
-
-pdf("results/figures/clustering_artworks.pdf", height = 8, width = 8, pointsize = 16)
-#png("results/figures/clustering_artworks.png", units = "in", height = 8, width = 8, pointsize = 16, res = 300)
-
-par(mai = c(.6,.6,.1,.1), mgp = c(2.4, 1, 0))
-
-plot(coor_2d, type = "n", ylim = c(-3.7, 2.6), xlim = c(-5, 10.5),
-     xlab = "", ylab = "")
-
-for (item in items) {
-
-  if (item == "125") {
-
-    pic <- jpeg::readJPEG(paste0("../data/haum/ContentEyevisit/eyevisit_cards_light/",
-                          item, "/", item, ".jpg"))
-  } else {
-    pic <- png::readPNG(paste0("../data/haum/ContentEyevisit/eyevisit_cards_light/",
-                        item, "/", item, ".png"))
-  }
-
-  img <- as.raster(pic[,,1:3])
-
-  x <- coor_2d[items == item, 1]
-  y <- coor_2d[items == item, 2]
-
-  points(x, y,
-         col = mycols[cluster[items == item]],
-         cex = 6,
-         pch = 15)
-
-  rasterImage(img,
-              xleft = x - .45,
-              xright = x + .45,
-              ybottom = y - .2,
-              ytop = y + .2)
-
-}
-legend("topright", paste("Cluster", 1:k), col = mycols, pch = 15, bty = "n")
-
-dev.off()
+save(hc, k, res, dist_mat, datitem, df,
+     file = "../../thesis/figures/data/clustering_items.RData")