diff --git a/code/00_pm.py b/code/00_pm.py
index ec11bdd..7324041 100644
--- a/code/00_pm.py
+++ b/code/00_pm.py
@@ -49,12 +49,18 @@ h_eval = eval_pm(event_log, net, im, fm)
 pm4py.vis.view_petri_net(net, im, fm)
 pm4py.vis.save_vis_petri_net(net, im, fm, "../figures/processmaps/pn_heuristics_complete.png")
 
+is_sound = pm4py.check_soundness(net, im, fm)
+
 # decorated petri net
 from pm4py.visualization.petri_net import visualizer as pn_visualizer
 parameters = {pn_visualizer.Variants.FREQUENCY.value.Parameters.FORMAT: "png"}
 gviz = pn_visualizer.apply(net, im, fm, parameters=parameters, variant=pn_visualizer.Variants.FREQUENCY, log=event_log)
 pn_visualizer.save(gviz, "../figures/processmaps/pn_heuristics_complete_decorated.png")
 
+# convert to process tree
+bpmn = pm4py.convert.convert_to_bpmn(net, im, fm)
+pm4py.vis.view_bpmn(bpmn)
+
 ## Alpha Miner
 net, im, fm = pm4py.discover_petri_net_alpha(event_log)
 a_eval = eval_pm(event_log, net, im, fm)
@@ -67,6 +73,9 @@ i_eval = eval_pm(event_log, net, im, fm)
 pm4py.vis.view_petri_net(net, im, fm)
 pm4py.vis.save_vis_petri_net(net, im, fm, "../figures/processmaps/pn_induction_complete.png")
 
+# as process tree (does not work for heuristics miner!)
+pt = pm4py.discover_process_tree_inductive(event_log)
+pm4py.vis.view_process_tree(pt)
 
 ## ILP Miner
 net, im, fm = pm4py.discover_petri_net_ilp(event_log)
diff --git a/code/01_clustering.R b/code/01_clustering.R
index 7bafa80..ff3ec2a 100644
--- a/code/01_clustering.R
+++ b/code/01_clustering.R
@@ -1,14 +1,25 @@
-# 00_current_analysis.R
+# 01_clustering.R
 #
-# content: (1) Read evalutation data
+# content: (1) Read evaluation data
 #          (2) Clustering
 #          (3) Visualization with pictures
+#          (4) Read event logs
+#          (5) Frequency plot for clusters
+#          (6) DFGs for clusters
 #
-# input: results/eval_heuristics_artworks.csv
-#        results/eval_all-miners_complete.csv
-# output: --
+# input:  results/eval_heuristics_artworks.csv
+#         results/eval_all-miners_complete.csv
+#         ../data/haum/event_logfiles_glossar_2023-11-03_17-46-28.csv
+# output: ../figures/clustering_heuristics.pdf
+#         ../figures/clustering_heuristics.png
+#         ../figures/processmaps/dfg_complete_R.pdf
+#         ../figures/processmaps/dfg_complete_R.png
+#         ../figures/processmaps/dfg_cluster1_R.pdf
+#         ../figures/processmaps/dfg_cluster2_R.pdf
+#         ../figures/processmaps/dfg_cluster3_R.pdf
+#         ../figures/processmaps/dfg_cluster4_R.pdf
 #
-# last mod: 2023-12-08, NW
+# last mod: 2023-12-21, NW
 
 # setwd("C:/Users/nwickelmaier/Nextcloud/Documents/MDS/2023ss/60100_master_thesis/code")
 
@@ -162,15 +173,22 @@ dat_count$cluster <- factor(dat_count$cluster, levels = c(4, 2, 1, 3), labels =
 dat_count <- dat_count[order(dat_count$cluster, dat_count$freq, decreasing = TRUE), ]
 dat_count$artwork <- factor(dat_count$artwork, levels = unique(dat_count$artwork))
 
+png("../figures/counts_artworks_clusters.png", units = "in", height = 3.375, width = 12, pointsize = 10, res = 300)
+par(mai = c(.6,.6,.1,.1), mgp = c(2.4, 1, 0))
 barplot(freq ~ artwork, dat_count, las = 2, ylim = c(0, 60000),
         border = "white", ylab = "",
         col = c("#FF6900", "#78004B", "#3CB4DC", "#91C86E" )[dat_count$cluster])
+dev.off()
 
 # compare to clusters
+
+png("../figures/pm_heuristics_clusters.png", units = "in", height = 3.375, width = 3.375, pointsize = 10, res = 300)
+par(mai = c(.6,.6,.1,.1), mgp = c(2.4, 1, 0))
 plot(generalizability ~ precision, eval_heuristics, type = "n", ylim = c(0.845, 0.98))
 with(eval_heuristics, text(precision, generalizability,
                            rownames(eval_heuristics),
                            col = colors[k1$cluster]))
+dev.off()
 
 #--------------- (6) DFGs for clusters ---------------
 
@@ -202,12 +220,16 @@ dfg_complete <- process_map(alog,
             sec_nodes  = frequency("relative"),
             type_edges = frequency("absolute", color_edges = "#FF6900"),
             sec_edges  = frequency("relative"),
-            rankdir    = "TB",
+            #rankdir    = "TB",
             render     = FALSE)
 export_map(dfg_complete,
            file_name = "../figures/processmaps/dfg_complete_R.pdf",
            file_type = "pdf",
            title     = "DFG complete")
+export_map(dfg_complete,
+           file_name = "../figures/processmaps/dfg_complete_R.png",
+           file_type = "png")
+
 dfg_c1 <- process_map(alog_c1,
             type_nodes = frequency("absolute", color_scale = "Greys"),
             sec_nodes  = frequency("relative"),