From db546b208d483b9c98f67f3d34ece9cdab31d400 Mon Sep 17 00:00:00 2001
From: ben-jaynes <1btjaynes@gmail.com>
Date: Thu, 28 May 2026 18:55:41 -0700
Subject: [PATCH] vault backup: 2026-05-28 18:55:41

---
 .../Class 5-28 (Clustering).md                 | 18 ++++++++++++++++--
 1 file changed, 16 insertions(+), 2 deletions(-)

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 $$I_{i}=\frac{\sum_{j \in C_{i}}|\overline X_{j} - X_{i}|^2}{n_{i}}$$
-		- 
\ No newline at end of file
+		- 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
\ No newline at end of file