diff --git a/Running Start/CSB320 - Machine Learning Concepts/Class 5-28 (Clustering).md b/Running Start/CSB320 - Machine Learning Concepts/Class 5-28 (Clustering).md
index bbac9e7..54a723f 100644
--- a/Running Start/CSB320 - Machine Learning Concepts/Class 5-28 (Clustering).md	
+++ b/Running Start/CSB320 - Machine Learning Concepts/Class 5-28 (Clustering).md	
@@ -17,5 +17,19 @@
 	- centroid
 		- mean position of a cluster's instances $$\overline X_{i}=\frac{\sum_{j \in C_{i}}X_{i}}{n_{i}}$$
 	- Inertia
-		- aerage squared distance of the instances from the centroid $$I_{i}=\frac{\sum_{j \in C_{i}}|\overline X_{j} - X_{i}|^2}{n_{i}}$$
-		- 
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+		- average squared distance of the instances from the centroid $$I_{i}=\frac{\sum_{j \in C_{i}}|\overline X_{j} - X_{i}|^2}{n_{i}}$$
+	- partitioning approach
+		- create various partitions and evaluate based on metric (minimize sum of squared errors)
+	- K-means clustering
+		- assigns instances to the nearest centroid
+		- need to know k number of clusters beforehand
+		- algorithm
+			- k points are chosen randomly as initial centroids
+			- assign every data point to the closest centroid
+			- compute new centroids with assigned data
+			- if centroids don't change, stop. If they do repeat with new centroids
+		- Choosing the optimal number of clusters
+			- elbow method
+				- graph inertia against k and find point where elbow of data is (curve levels off)
+			- Silhouette method
+				- Use silhouette coeffic
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