diff --git a/03_CAH_clust.R b/03_CAH_clust_toutes_dominantes.R
similarity index 79%
rename from 03_CAH_clust.R
rename to 03_CAH_clust_toutes_dominantes.R
index 08ddfaa..ca0780e 100644
--- a/03_CAH_clust.R
+++ b/03_CAH_clust_toutes_dominantes.R
@@ -6,7 +6,7 @@ library(here)
 library(ggplot2)
 library(ggdendro)
 library(factoextra)
-
+set.seed(1234)
 data <- data.frame(read.csv(file.path(here(), "data", "02_preprocessed_data.csv"),
     header = TRUE
 ), stringsAsFactors = TRUE)
@@ -23,7 +23,7 @@ data <- data %>%
     mutate_if(sapply(data, is.character), as.factor)
 
 selected_cols <- c(
-    "parcours", "domaine2A", "1AC-MI",
+    "dominante3A", "parcours", "domaine2A", "1AC-MI",
     "2A-UEchoix-S1-UC1", "2A-UEchoix-S1-UC2", "2A-UEchoix-S2-UC4",
     "2A-UEchoix-S2-UC3", "2A-UEchoix-S2-UC5", "2A-UEchoix-S2-UC6",
     "2A-Projet-S2", "1AC-OUVERTURE"
@@ -32,7 +32,7 @@ selected_cols <- c(
 onehot_data <- one_hot(as.data.table(data), cols = selected_cols, sparsifyNAs = TRUE)
 
 #  Fonctionne bien avec binary
-dist_eucl <- dist(x = onehot_data[, -c(1, 2, 3)], method = "binary")
+dist_eucl <- dist(x = onehot_data[, - 1], method = "binary")
 
 hclust_avg <- hclust(dist_eucl, method = "average")
 
@@ -45,10 +45,12 @@ p <- ggplot(segment(plotdata)) +
     scale_y_reverse(expand = c(0.2, 0))
 p + theme_dendro()
 
-fviz_nbclust(onehot_data[, -c(1,2,3)], FUNcluster = hcut, k.max = 30)
+fviz_nbclust(onehot_data[, -c(1, 2, 3)], FUNcluster = hcut, k.max = 30, method = "wss")
 
 cut_avg <- cutree(hclust_avg, k = 6)
 names(cut_avg) <- data[["ine"]]
 table(cut_avg)
 
-onehot_data[["cluster"]] <- cut_avg
\ No newline at end of file
+data[["cluster"]] <- cut_avg
+
+write.csv(data, file.path(here(), "data", "03_cah_results.csv"), row.names = FALSE)
diff --git a/04_CAH_clust_par_dominantes.R b/04_CAH_clust_par_dominantes.R
new file mode 100644
index 0000000..ec939a8
--- /dev/null
+++ b/04_CAH_clust_par_dominantes.R
@@ -0,0 +1,89 @@
+library(data.table)
+library(mltools)
+library(dplyr)
+library(tidyr)
+library(here)
+library(ggplot2)
+library(ggdendro)
+library(factoextra)
+
+set.seed(1234)
+data <- data.frame(read.csv(file.path(here(), "data", "02_preprocessed_data.csv"),
+    header = TRUE
+), stringsAsFactors = TRUE)
+
+data_folder_path <- file.path(here(), "data", "04_CAH_par_dominante")
+
+if (!dir.exists(data_folder_path)) {
+    dir.create(data_folder_path)
+}
+
+colnames(data)[5:ncol(data)] <- c(
+    "1AC-OUVERTURE", "1AC-MI",
+    "2A-UEchoix-S1-UC1", "2A-UEchoix-S1-UC2", "2A-UEchoix-S2-UC3",
+    "2A-UEchoix-S2-UC4",
+    "2A-UEchoix-S2-UC5", "2A-UEchoix-S2-UC6",
+    "2A-Projet-S2"
+)
+
+data <- data %>%
+    mutate_if(sapply(data, is.character), as.factor)
+
+selected_cols <- c(
+    "dominante3A", "parcours", "domaine2A", "1AC-MI",
+    "2A-UEchoix-S1-UC1", "2A-UEchoix-S1-UC2", "2A-UEchoix-S2-UC4",
+    "2A-UEchoix-S2-UC3", "2A-UEchoix-S2-UC5", "2A-UEchoix-S2-UC6",
+    "2A-Projet-S2", "1AC-OUVERTURE"
+)
+
+seuil_effectif <- 20L
+
+#  On récupère les dominantes supérieures au seuil
+vec_dominantes <- na.omit(sapply(unique(data[["dominante3A"]]), function(dominante) {
+    ifelse(sum(data[["dominante3A"]] == dominante) > seuil_effectif,
+        as.character(dominante), NA
+    )
+}))
+
+for (dominante in vec_dominantes) {
+    message("Dominante ", dominante)
+    data_dominante <- data %>% filter(dominante3A == dominante)
+    onehot_data <- one_hot(as.data.table(data_dominante), cols = selected_cols, sparsifyNAs = TRUE)
+
+    #  Fonctionne bien avec binary
+    dist <- dist(x = onehot_data[, -1], method = "binary")
+
+    hclust_avg <- hclust(dist, method = "average")
+
+    dhc <- as.dendrogram(hclust_avg)
+
+    plotdata <- dendro_data(dhc, type = "rectangle")
+    p <- ggplot(segment(plotdata)) +
+        geom_segment(aes(x = x, y = y, xend = xend, yend = yend)) +
+        coord_flip() +
+        scale_y_reverse(expand = c(0.2, 0)) +
+        ggtitle(paste0("Dominante : ", dominante))
+    p + theme_dendro()
+
+    print(fviz_nbclust(onehot_data,
+        FUNcluster = hcut
+    )+
+        ggtitle(paste0("Dominante : ", dominante)))
+
+    nb_clust_dominante <- as.integer(readline(prompt = paste0(
+        "Nb clusters ",
+        dominante,
+        " : "
+    )))
+
+    cut_avg <- cutree(hclust_avg, k = nb_clust_dominante)
+    names(cut_avg) <- data_dominante[["ine"]]
+    table(cut_avg)
+
+    data_dominante[["cluster"]] <- cut_avg
+
+    write.csv(data_dominante, file.path(
+        data_folder_path,
+        paste0("04_", dominante, "_cah_results.csv")
+    ), row.names = FALSE)
+}