vault backup: 2026-05-28 19:36:01

.obsidian/plugins/harper/data.json

@@ -1,5 +1,5 @@
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@@ -434,6 +434,8 @@
     "frc",
     "hea150",
     "math163",
+    "medioid",
+    "medioids",
     "parallelpiped",
     "pcb"
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Running Start/CSB320 - Machine Learning Concepts/Class 5-28 (Clustering).md

@@ -36,4 +36,17 @@
 				- $\overline{d_{out}(j)}$ is the average distance of the instance $i$ to the centroid of all other clusters
 				- $\overline{d_{in}(j)}$ is the average distance of the instance $i$ to all other instances in its cluster
 				- ![[SilhouetteMethod.excalidraw]]
-				- 
+		- effective for small/medium datasets
+		- assumes spherical clusters
+		- sensitive to initial conditions
+		- equal-sized groups assumption
+		- sensitive to outliers
+			- k-medioids can be used
+				- medioid is the most centrally located point in a cluster
+				- PAM: partitioning around medioids
+	- Hierarchical clustering
+		- does not require number of clusters, requires stopping point
+		- Agglomerative
+			- starts with each instance as cluster and merges into larger clusters
+		- divisive
+			- starts with one cluster and splits into