From 82946773be1899740823b00930ed49615a72410f Mon Sep 17 00:00:00 2001
From: celopezg <celopezg@unal.edu.co>
Date: Sun, 5 Aug 2018 10:21:09 -0500
Subject: [PATCH] typo

---
 kmeans.Rmd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/kmeans.Rmd b/kmeans.Rmd
index b1b6852..7a28940 100644
--- a/kmeans.Rmd
+++ b/kmeans.Rmd
@@ -23,7 +23,7 @@ The outline of the algorithm is
 
 3. Assign points to their closest centroid; cluster membership corresponds to the centroid assignment
 
-4. Reclaculate centroid positions and repeat.
+4. Recalculate centroid positions and repeat
 
 
 This approach, like most clustering methods requires a defined distance metric, a fixed number of clusters, and an initial guess as to the cluster centriods. There's no set approach to determining the initial configuration of centroids, but many algorithms simply randomly select data points from your dataset as the initial centroids.