diff --git a/Runtime-and-CoreCount.ipynb b/Runtime-and-CoreCount.ipynb
index 88f5862e7d5ed401ae7aa43d4873d12083bef9db..8448720fa0c8b782b5cfab175d8ecf0d591b2a7f 100644
--- a/Runtime-and-CoreCount.ipynb
+++ b/Runtime-and-CoreCount.ipynb
@@ -359,6 +359,19 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "column_maxes_runtime = df_runtime_cluster.max()\n",
+    "df_runtime_cluster_max = column_maxes_runtime.max()\n",
+    "normalized_runtime_df = df_runtime_cluster / df_runtime_cluster_max\n",
+    "\n",
+    "print(normalized_runtime_df)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -384,8 +397,8 @@
     "\n",
     "# sets to clusters and returns the cluster points\n",
     "kmeans_elapsed_reqmem = KMeans(n_clusters=3, random_state=111)\n",
-    "kmeans_elapsed_reqmem.fit(df_runtime_cluster)\n",
-    "print(kmeans_elapsed_reqmem.cluster_centers_)"
+    "kmeans_elapsed_reqmem.fit(normalized_runtime_df)\n",
+    "clusterpoints_elapsed_reqmem = kmeans_elapsed_reqmem.cluster_centers_ * df_runtime_cluster_max"
    ]
   },
   {
@@ -489,7 +502,7 @@
     "\n",
     "elapsed_rqmem_clustergraph = figure.add_subplot(121)\n",
     "elapsed_rqmem_clustergraph.scatter(df_runtime_cluster['ReqMemCPU'],df_runtime_cluster['Elapsed'], c=kmeans_elapsed_reqmem.labels_, cmap='rainbow')\n",
-    "elapsed_rqmem_clustergraph.scatter(kmeans_elapsed_reqmem.cluster_centers_[:,0] ,kmeans_elapsed_reqmem.cluster_centers_[:,1], color='black')\n",
+    "elapsed_rqmem_clustergraph.scatter(clusterpoints_elapsed_reqmem[:,0] ,clusterpoints_elapsed_reqmem[:,1], color='black')\n",
     "plt.xlabel('ReqMemCPU(gigs)')\n",
     "plt.ylabel('Elapsed(hours)')\n",
     "\n",
@@ -497,7 +510,7 @@
     "elapsed_rqmem_clustergraph_3d = figure.add_subplot(122, projection='3d')\n",
     "elapsed_rqmem_clustergraph_3d.scatter(df_runtime_cluster['ReqMemCPU'], df_runtime_cluster['Elapsed'], df_runtime_cluster['AllocCPUS'], \n",
     "                                      c=kmeans_elapsed_reqmem.labels_ ,cmap='rainbow')\n",
-    "elapsed_rqmem_clustergraph_3d.scatter(kmeans_elapsed_reqmem.cluster_centers_[:,0] ,kmeans_elapsed_reqmem.cluster_centers_[:,1], color='black')\n",
+    "elapsed_rqmem_clustergraph_3d.scatter(clusterpoints_elapsed_reqmem[:,0] ,clusterpoints_elapsed_reqmem[:,1], color='black')\n",
     "\n",
     "\n",
     "elapsed_rqmem_clustergraph_3d.set_xlabel('ReqMemCPU(gigs)')\n",
@@ -509,7 +522,7 @@
     "elapsed_rqmem_clustergraph_3d.yaxis._axinfo[\"grid\"].update({\"linewidth\":.5, \"color\" : \"black\"})\n",
     "elapsed_rqmem_clustergraph_3d.zaxis._axinfo[\"grid\"].update({\"linewidth\":.5, \"color\" : \"black\"})\n",
     "\n",
-    "plt.show()\n"
+    "plt.show()"
    ]
   },
   {
@@ -609,8 +622,8 @@
     "\n",
     "# sets to clusters and returns the cluster points\n",
     "kmeans_elapsed_alloc = KMeans(n_clusters=3, random_state=111)\n",
-    "kmeans_elapsed_alloc.fit(df_runtime_cluster)\n",
-    "print(kmeans_elapsed_alloc.cluster_centers_)"
+    "kmeans_elapsed_alloc.fit(normalized_runtime_df)\n",
+    "clusterpoints_elapsed_alloc = kmeans_elapsed_alloc.cluster_centers_ * df_runtime_cluster_max"
    ]
   },
   {
@@ -714,14 +727,14 @@
     "\n",
     "elapsed_alloc_clustergraph = figure.add_subplot(121)\n",
     "elapsed_alloc_clustergraph.scatter(df_runtime_cluster['AllocCPUS'],df_runtime_cluster['Elapsed'], c=kmeans_elapsed_alloc.labels_, cmap='rainbow')\n",
-    "elapsed_alloc_clustergraph.scatter(kmeans_elapsed_alloc.cluster_centers_[:,0] ,kmeans_elapsed_alloc.cluster_centers_[:,1], color='black')\n",
+    "elapsed_alloc_clustergraph.scatter(clusterpoints_elapsed_alloc[:,0] ,clusterpoints_elapsed_alloc[:,1], color='black')\n",
     "plt.xlabel('AllocCPUS')\n",
     "plt.ylabel('Elapsed(hours)')\n",
     "\n",
     "# 3d veiw of the scatterplot for better understanding of the data\n",
     "elapsed_alloc_clustergraph_3d = figure.add_subplot(122, projection='3d')\n",
     "elapsed_alloc_clustergraph_3d.scatter(df_runtime_cluster['AllocCPUS'], df_runtime_cluster['Elapsed'], df_runtime_cluster['ReqMemCPU'], c=kmeans_elapsed_alloc.labels_ ,cmap='rainbow')\n",
-    "elapsed_alloc_clustergraph_3d.scatter(kmeans_elapsed_alloc.cluster_centers_[:,0] ,kmeans_elapsed_alloc.cluster_centers_[:,1], color='black')\n",
+    "elapsed_alloc_clustergraph_3d.scatter(clusterpoints_elapsed_alloc[:,0] ,clusterpoints_elapsed_alloc[:,1], color='black')\n",
     "elapsed_alloc_clustergraph_3d.set_xlabel('AllocCPUS')\n",
     "elapsed_alloc_clustergraph_3d.set_ylabel('Elapsed(hours)')\n",
     "elapsed_alloc_clustergraph_3d.set_zlabel('ReqMemCPU(gigs)')\n",
@@ -881,11 +894,16 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "The next 5 cells create the clusters, find each cluster label, and create datasets of data in each cluster.\n",
-    "All the datasets are created for both the cluster graphs and plots of each cluster before those graphs are made."
+    "column_maxes_alloc = df_alloc_cluster.max()\n",
+    "df_alloc_cluster_max = column_maxes_alloc.max()\n",
+    "normalized_alloc_df = df_alloc_cluster / df_alloc_cluster_max\n",
+    "\n",
+    "print(normalized_alloc_df)"
    ]
   },
   {
@@ -898,8 +916,16 @@
     "\n",
     "# sets to clusters and returns the cluster points\n",
     "kmeans_alloc_reqmem = KMeans(n_clusters=3, random_state=111)\n",
-    "kmeans_alloc_reqmem.fit(df_alloc_cluster)\n",
-    "print(kmeans_alloc_reqmem.cluster_centers_)"
+    "kmeans_alloc_reqmem.fit(normalized_alloc_df)\n",
+    "clusterpoints_alloc_reqmem = kmeans_alloc_reqmem.cluster_centers_ * df_alloc_cluster_max"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The next 5 cells create the clusters, find each cluster label, and create datasets of data in each cluster.\n",
+    "All the datasets are created for both the cluster graphs and plots of each cluster before those graphs are made."
    ]
   },
   {
@@ -915,7 +941,7 @@
     "# 0 = purple cluster\n",
     "# 1 = green cluster\n",
     "# 2 = red cluster\n",
-    "np.unique(kmeans_elapsed_alloc.labels_)"
+    "np.unique(kmeans_alloc_reqmem.labels_)"
    ]
   },
   {
@@ -1003,14 +1029,14 @@
     "\n",
     "alloc_reqmem_cluster_graph = figure.add_subplot(121)\n",
     "alloc_reqmem_cluster_graph.scatter(df_alloc_cluster['ReqMemCPU'],df_alloc_cluster['AllocCPUS'], c=kmeans_alloc_reqmem.labels_, cmap='rainbow')\n",
-    "alloc_reqmem_cluster_graph.scatter(kmeans_alloc_reqmem.cluster_centers_[:,0] ,kmeans_alloc_reqmem.cluster_centers_[:,1], color='black')\n",
+    "alloc_reqmem_cluster_graph.scatter(clusterpoints_alloc_reqmem[:,0] ,clusterpoints_alloc_reqmem[:,1], color='black')\n",
     "plt.xlabel('ReqMemCPU(gigs)')\n",
     "plt.ylabel('AllocCPUS')\n",
     "\n",
     "# 3d veiw of the scatterplot for better understanding of the data\n",
     "alloc_reqmem_clustergraph_3d = figure.add_subplot(122, projection='3d')\n",
     "alloc_reqmem_clustergraph_3d.scatter(df_alloc_cluster['ReqMemCPU'], df_alloc_cluster['AllocCPUS'], df_alloc_cluster['Elapsed'], c=kmeans_alloc_reqmem.labels_ ,cmap='rainbow')\n",
-    "alloc_reqmem_clustergraph_3d.scatter(kmeans_alloc_reqmem.cluster_centers_[:,0] ,kmeans_alloc_reqmem.cluster_centers_[:,1], color='black')\n",
+    "alloc_reqmem_clustergraph_3d.scatter(clusterpoints_alloc_reqmem[:,0] ,clusterpoints_alloc_reqmem[:,1], color='black')\n",
     "alloc_reqmem_clustergraph_3d.set_xlabel('ReqMemCPU(gigs')\n",
     "alloc_reqmem_clustergraph_3d.set_ylabel('AllocCPUS')\n",
     "alloc_reqmem_clustergraph_3d.set_zlabel('Elapsed(hours)')\n",