Minor edits

Author: GraemeMalcolm

04 - Clustering.ipynb

@@ -222,7 +222,7 @@
         "\n",
         "Hierarchical clustering methods make fewer distributional assumptions when compared to K-means methods. However, K-means methods are generally more scalable, sometimes very much so.\n",
         "\n",
-        "Hierarchical clustering creates clusters by either a divisive method or agglomerative method. The divisive method is a \"top down\" approach starting with the entire dataset and then finding partitions in a stepwise manner. The later method is known as agglomerative clustering is a \"bottom up** approach. In this lab you will work with agglomerative clustering which roughly works as follows:\n",
+        "Hierarchical clustering creates clusters by either a *divisive* method or *agglomerative* method. The divisive method is a \"top down\" approach starting with the entire dataset and then finding partitions in a stepwise manner. Agglomerative clustering is a \"bottom up** approach. In this lab you will work with agglomerative clustering which roughly works as follows:\n",
         "\n",
         "1. The linkage distances between each of the data points is computed.\n",
         "2. Points are clustered pairwise with their nearest neighbor.\n",