diff --git a/coral/sql.py b/coral/sql.py
index 280d6db..a8c58d8 100644
--- a/coral/sql.py
+++ b/coral/sql.py
@@ -17,6 +17,10 @@
 
 
 DEBUG = False
+HDBSCAN = "hdbscan"
+K_PROTOTYPES = "k-prototypes"
+K_MODES = "k-modes"
+K_MEANS = "k-means"
 
 if DEBUG:
 # for debugging. Shut down api-1-container
@@ -883,46 +887,7 @@ def recommendSplit():
         if int(request.values["numberOfClusters"]) > 0:
             optimal_k = int(request.values["numberOfClusters"])
         else:
-            # determine useful number of clusters
-            # get the optimal number of clusters
-            n_clusters_range = range(2, 60)  # arbitrarily chosen (talk to the user about this)
-
-            # Calculate within-cluster sum of squares (inertia) for different k values
-            inertia_values = []
-            for k in n_clusters_range:
-                kmeans = KMeans(n_clusters=k, n_init='auto')
-                kmeans.fit(tissues_attribute_df)
-                inertia_values.append(kmeans.inertia_)
-
-            # Calculate the rate of change of inertia
-            # Inertia measures how well a dataset was clustered by K-Means. It is calculated by measuring the distance between each data point and its centroid, squaring this distance, and summing these squares across one cluster.
-            # https://www.codecademy.com/learn/machine-learning/modules/dspath-clustering/cheatsheet
-            rate_of_change = np.diff(inertia_values)  # rate of change from 1 to 2, 2 to 3, etc
-
-            # Find the "elbow point" where the rate of change starts to slow down
-            # Calculate the "elbow point" where the rate of change slows down
-            # if no elbow_point is found, the last index of inertia_values is chosen
-            elbow_point = len(inertia_values) - 1
-
-            _log.debug("rate_of_change %s", rate_of_change)
-            _log.debug("inertia_values %s", inertia_values)
-
-            for i in range(len(rate_of_change) - 1):
-                diff1 = rate_of_change[i]
-                diff2 = rate_of_change[i + 1]
-                change_ratio = diff2 / diff1
-                _log.debug("change_ratio %s", change_ratio)
-                if change_ratio < 0.1:  # this is an "arbitrary" threshold. The smaller the threshold, the more clusters are chosen
-                    elbow_point = i  # the rate_of_change show e.g. the change from 3 clusters to 4 cluster in index 2 of rate_of_change.
-                    # so the elbow point is the index of the rate_of_change where the change from e.g. 3 to 4 is not big anymore: index 2. 3 clusters is a good amount of clusters.
-                    break
-
-            optimal_k = n_clusters_range[elbow_point]  # e.g. at the elbow point: 2 the number of clusters is 3
-
-            # somehow, for this data, this approach does not work. The change_ratio does not start high and go down, but is e.g. 0.40, 0.49, 0.61, 0.63, 0.59, 0.79, 0.49, 1.37, etc
-
-            _log.debug("Optimal number of clusters: %s", optimal_k)
-
+            optimal_k = k_ellbow(tissues_attribute_df, range(2, 10), K_MEANS)
         # do the clustering with the optimal or userdefined number of clusters
         clusterer_attribute_kmeans = KMeans(n_clusters=optimal_k, n_init='auto')
         clusterer_attribute_kmeans.fit(tissues_attribute_df)
@@ -991,6 +956,58 @@ def recommendSplit():
         abort(400, error)
 
 
+# Helper function to determine a useful number of clusters
+def k_ellbow(tissues_attribute_df, n_clusters_range=range(2, 60), cluster_method=K_MEANS, position_of_cat_attr=None):
+
+    # Calculate within-cluster sum of squares (inertia) for different k values
+    inertia_values = []
+    for k in n_clusters_range:
+        if cluster_method == K_MEANS:
+            kmeans = KMeans(n_clusters=k, n_init='auto')
+            kmeans.fit(tissues_attribute_df)
+            inertia_values.append(kmeans.inertia_)
+            # Inertia measures how well a dataset was clustered by K-Means. 
+            # It is calculated by measuring the distance between each data point and its centroid, squaring this distance, and summing these squares across one cluster.
+        elif cluster_method == K_PROTOTYPES:
+            clusterer = KPrototypes(n_clusters=k, init='Cao', n_init=1, verbose=2)
+            clusterer.fit(tissues_attribute_df, categorical=position_of_cat_attr)
+            inertia_values.append(clusterer.cost_) 
+            # is cost_ the right value to use here?
+            # For the K-prototypes function, cost is defined as the sum distance of all points to their respective cluster centroids.
+            # ==> same as inertia
+
+    # Calculate the rate of change of inertia
+    # Inertia measures how well a dataset was clustered by K-Means. It is calculated by measuring the distance between each data point and its centroid, squaring this distance, and summing these squares across one cluster.
+    # https://www.codecademy.com/learn/machine-learning/modules/dspath-clustering/cheatsheet
+    rate_of_change = np.diff(inertia_values)  # rate of change from 1 to 2, 2 to 3, etc
+
+    # Find the "elbow point" where the rate of change starts to slow down
+    # Calculate the "elbow point" where the rate of change slows down
+    # if no elbow_point is found, the last index of inertia_values is chosen
+    elbow_point = len(inertia_values) - 1
+
+    _log.debug("rate_of_change %s", rate_of_change)
+    _log.debug("inertia_values %s", inertia_values)
+
+    for i in range(len(rate_of_change) - 1):
+        diff1 = rate_of_change[i]
+        diff2 = rate_of_change[i + 1]
+        change_ratio = diff2 / diff1
+        _log.debug("change_ratio %s", change_ratio)
+        if change_ratio < 0.1:  # this is an "arbitrary" threshold. The smaller the threshold, the more clusters are chosen
+            elbow_point = i  # the rate_of_change show e.g. the change from 3 clusters to 4 cluster in index 2 of rate_of_change.
+            # so the elbow point is the index of the rate_of_change where the change from e.g. 3 to 4 is not big anymore: index 2. 3 clusters is a good amount of clusters.
+            break
+
+    optimal_k = n_clusters_range[elbow_point]  # e.g. at the elbow point: 2 the number of clusters is 3
+
+    # somehow, for this data, this approach does not work. The change_ratio does not start high and go down, but is e.g. 0.40, 0.49, 0.61, 0.63, 0.59, 0.79, 0.49, 1.37, etc
+
+    _log.debug("Optimal number of clusters: %s", optimal_k)
+    return optimal_k
+
+
+
 @app.route("/createAutomatically", methods=["GET", "POST"])
 @login_required
 def create_automatically():
@@ -1013,10 +1030,6 @@ def create_automatically():
         # if there is just one (numerical) attribute, use hdbscan
         # if there are two (numerical) attributes, use hdbscan
         # if there is one categorical and one numerical attribute, use k-prototypes
-        HDBSCAN = "hdbscan"
-        K_PROTOTYPES = "k-prototypes"
-        K_MODES = "k-modes"
-        K_MEANS = "k-means"
         cluster_method = None
 
         query = QueryElements()
@@ -1076,15 +1089,16 @@ def create_automatically():
             # kmeans end
             # add the cluster labels to the tissues
         elif cluster_method == K_PROTOTYPES:
-            # 1 categorical and 1 numerical attribute ==> k-prototypes
-            # get the numerical and the categorical attributes
-            # get the numerical attributes and categorical attributes from tissues_attriubte_df according to the attribute types
-            position_of_cat_attr = 0
-            if attribute0["type"] == "number":
-                position_of_cat_attr = 1
-            num_clusters = 2
-            clusterer = KPrototypes(n_clusters=num_clusters, init='Cao', n_init=1, verbose=2)
-            clusterer.fit(tissues_attribute_df, categorical=[position_of_cat_attr])
+            # find the positions of the categorical attributes
+            position_of_cat_attr = []
+            for i in range(len(attributes)):
+                if attributes[i]["type"] == "categorical":
+                    position_of_cat_attr.append(i)
+            if number_of_clusters == 0:
+                # determine optimal number of clusters with ellbow method:
+                number_of_clusters = k_ellbow(tissues_attribute_df, range(2, 20), K_PROTOTYPES, position_of_cat_attr)
+            clusterer = KPrototypes(n_clusters=number_of_clusters, init='Cao', n_init=1, verbose=2)
+            clusterer.fit(tissues_attribute_df, categorical=position_of_cat_attr)
             # Get cluster labels
             labels = clusterer.labels_
         elif cluster_method == K_MODES:
@@ -1136,6 +1150,7 @@ def create_automatically():
         abort(400, error)
 
 
+
 # saved this just before for experimenting with attributes array
 # def create_automatically():
 #     # error msg is wrong, it is based on the cohortData route
diff --git a/src/Taskview/tasks/Filter.ts b/src/Taskview/tasks/Filter.ts
index 7c1bc88..c48cb58 100644
--- a/src/Taskview/tasks/Filter.ts
+++ b/src/Taskview/tasks/Filter.ts
@@ -162,9 +162,12 @@ export class Filter extends ATask {
     this.controls.insertAdjacentHTML(
       'afterbegin',
       `
-    <div>
-      <button type="button" class="btn createAutomaticallyBtn btn-coral-prime" title="Calculate meaningful splits.">Create cohorts automatically</button>
-    </div>
+      <div class="d-grid gap-2">
+        <button type="button" class="btn createAutomaticallyBtn btn-coral-prime" title="Calculate meaningful splits.">Create cohorts automatically</button>
+        <label>Number of Clusters</label>
+        <input type="number" class="clusters" step="any" min="1" max="99" value="2" />
+        <button type="button" class="btn createAutomaticallyWithNumberOfClustersBtn btn-coral-prime" title="Calculate meaningful splits.">Create cohorts for number of clusters</button>
+      </div>
     `,
     );
 
@@ -172,7 +175,14 @@ export class Filter extends ATask {
       .select('button.createAutomaticallyBtn')
       .on('click', () => {
         console.log("createAutomaticallyBtn clicked");
-        this.createAutomatically();
+        this.createAutomatically(false);
+      });
+
+    select(this.controls)
+      .select('button.createAutomaticallyWithNumberOfClustersBtn')
+      .on('click', () => {
+        console.log("createAutomaticallyWithNumberOfClustersBtn clicked");
+        this.createAutomatically(true);
       });
   }
 
@@ -183,12 +193,12 @@ export class Filter extends ATask {
     // todo: implement
 
     let numberOfClusters = 0;
-    // if (useNumberOfClusters) {
-    //   // select the bins field
-    //   // binsCount = (this.controls.querySelector('#split input.bins') as HTMLInputElement).valueAsNumber;
-    //   numberOfClusters = (this.controls.querySelector(`#split #recommendSplitControls input.clusters`) as HTMLInputElement).valueAsNumber;
-    //   console.log("numberOfClusters", numberOfClusters);
-    // }
+    if (useNumberOfClusters) {
+      // select the bins field
+      // let controls = this.controls;
+      numberOfClusters = (this.controls.querySelector(`.controls input.clusters`) as HTMLInputElement).valueAsNumber;
+      console.log("numberOfClusters", numberOfClusters);
+    }
 
     let newCohortIds = [];
     let attributesMapped = this.attributes.map((attr) => {return {dataKey: attr.dataKey, type: attr.type}});
@@ -205,22 +215,22 @@ export class Filter extends ATask {
       console.log("createAutomatically scatterplot data", newCohortIds);
     }
     // TODO: create the cohorts and show them
-    //
-    // let cohortDescs: INewCohortDesc[];
-    // cohortDescs = [];
-    // // for every selected cohort
-    // for (const cohort of this.cohorts) {
-    //   // for every newCohort create a filter (for now... the filter is actually not needed, will be changed in the future)
-    //   for (const newCohort of newCohortIds){
-    //     cohortDescs.push({
-    //       cohort: cohort,
-    //       newCohortId: newCohort,
-    //       attr:[this.attributes[0], this.attributes[1]]
-    //     });
-    //   }
-    // }
-    //
-    // this.container.dispatchEvent(new AutoSplitEvent(cohortDescs));
+
+    let cohortDescs: INewCohortDesc[];
+    cohortDescs = [];
+    // for every selected cohort
+    for (const cohort of this.cohorts) {
+      // for every newCohort create a filter (for now... the filter is actually not needed, will be changed in the future)
+      for (const newCohort of newCohortIds){
+        cohortDescs.push({
+          cohort: cohort,
+          newCohortId: newCohort,
+          attr:[this.attributes[0], this.attributes[1]]
+        });
+      }
+    }
+
+    this.container.dispatchEvent(new AutoSplitEvent(cohortDescs));
   }
 
 
diff --git a/src/Taskview/visualizations/GroupedBoxplot.ts b/src/Taskview/visualizations/GroupedBoxplot.ts
index d1ac15d..0444df8 100644
--- a/src/Taskview/visualizations/GroupedBoxplot.ts
+++ b/src/Taskview/visualizations/GroupedBoxplot.ts
@@ -242,18 +242,37 @@ export class GroupedBoxplot extends MultiAttributeVisualization {
     this.container.dispatchEvent(new SplitEvent(filterDescs));
   }
 
-  async createAutomatically() {
+  async createAutomatically(useNumberOfClusters: boolean = false) {
     console.log("createAutomatically GroupedBoxplot");
 
+    let numberOfClusters = 0;
+    if (useNumberOfClusters) {
+      // select the bins field
+      // binsCount = (this.controls.querySelector('#split input.bins') as HTMLInputElement).valueAsNumber;
+      numberOfClusters = (this.controls.querySelector(`#split #recommendSplitControls input.clusters`) as HTMLInputElement).valueAsNumber;
+      console.log("numberOfClusters", numberOfClusters);
+    }
+
     let newCohortIds = [];
+    let attributesMapped = this.attributes.map((attr) => {return {dataKey: attr.dataKey, type: attr.type}});
+    // convert the attributesParam to a JSON object
+    let attributesParam: string = JSON.stringify(attributesMapped);
     for (const cht of this.cohorts) {
+      // const params: ICohortMultiAttrDBDataParams = {
+      //   cohortId: cht.dbId,
+      //   attribute0: this.attributes[0].dataKey,
+      //   attribute0type: this.attributes[0].type,
+      //   attribute1: this.attributes[1].dataKey,
+      //   attribute1type: this.attributes[1].type,
+      //   numberOfClusters: numberOfClusters,
+      // };
+
       const params: ICohortMultiAttrDBDataParams = {
         cohortId: cht.dbId,
-        attribute0: this.attributes[0].dataKey,
-        attribute0type: this.attributes[0].type,
-        attribute1: this.attributes[1].dataKey,
-        attribute1type: this.attributes[1].type
+        attributes: attributesParam,
+        numberOfClusters: numberOfClusters,
       };
+
       newCohortIds = await createDBCohortAutomatically(params)
       console.log("createAutomatically scatterplot data", newCohortIds);
     }
diff --git a/src/Taskview/visualizations/MultiAttributeVisualization.ts b/src/Taskview/visualizations/MultiAttributeVisualization.ts
index eaacf5b..ccd14f8 100644
--- a/src/Taskview/visualizations/MultiAttributeVisualization.ts
+++ b/src/Taskview/visualizations/MultiAttributeVisualization.ts
@@ -155,11 +155,11 @@ export abstract class MultiAttributeVisualization extends AVegaVisualization {
         `beforeend`,
         `
     <div class="d-grid gap-2">
-      <button type="button" class="btn recommendSplitBtn btn-coral-prime btn-block" title="Calculate meaningful splits by choosing a useful number of clusters automatically.">Recommend split</button>
+<!--      <button type="button" class="btn recommendSplitBtn btn-coral-prime btn-block" title="Calculate meaningful splits by choosing a useful number of clusters automatically.">Recommend split</button>-->
       <button type="button" class="btn createAutomaticallyBtn btn-coral-prime" title="Calculate meaningful splits.">Create cohorts automatically</button>
       <label>Number of Clusters</label>
       <input type="number" class="clusters" step="any" min="1" max="99" value="2" />
-      <button type="button" class="btn recommendSplitWithNumberOfClustersBtn btn-coral-prime" title="Calculate meaningful splits according to the number of clusters selected.">Recommend split for number of clusters</button>
+<!--      <button type="button" class="btn recommendSplitWithNumberOfClustersBtn btn-coral-prime" title="Calculate meaningful splits according to the number of clusters selected.">Recommend split for number of clusters</button>-->
       <button type="button" class="btn createAutomaticallyWithNumberOfClustersBtn btn-coral-prime" title="Calculate meaningful splits.">Create cohorts for number of clusters</button>
     </div>
     `,