remove mention of random initialisation in the method and clarify wha…

…t convergence looks like, tasks 8 and 9

Picking initial points randomly here may be misleading for someone just looking up the method from this section. Have simply omitted and said that this is discussed below. Also, have removed the word convergence in favour of a description of what convergence looks like

_episodes_rmd/06-k-means.Rmd

@@ -75,9 +75,9 @@ The distance between data points within a cluster is
 used as a measure of within-cluster variation.
 
 To carry out K-means clustering, we first pick $k$ initial points as centres or 
-"centroids" of our clusters. There are a few ways to choose these initial "centroids",
-but for simplicity let's imagine we just pick three random co-ordinates.
-We then follow these two steps until convergence:
+"centroids" of our clusters. There are a few ways to choose these initial "centroids"
+and this is discussed below. Once we have picked intitial points, we then follow 
+these two steps until appropriate clusters have been formed:
 
 1. Assign each data point to the cluster with the closest centroid
 2. Update centroid positions as the average of the points in that cluster