From 4d53fa14ee0cd615444aca6f6ba176e0ccc1b5be Mon Sep 17 00:00:00 2001
From: TheSiahxyz <164138827+TheSiahxyz@users.noreply.github.com>
Date: Mon, 29 Apr 2024 22:06:12 -0400
Subject: init

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+---
+id: K-Means
+aliases: []
+tags:
+  - Clustering-Algorithms
+  - Compare-and-Contrast
+---
+
+- K-Means [(Youtube)](https://www.youtube.com/watch?v=KzJORp8bgqs)
+  - Each cluster is represented by the center/centroid of the cluster
+- Given K, the number of clusters, the _K-Means_ clustering algorithm is
+  outlined as follows
+  - Select _**K**_ points as initial centroids
+  - **Repeat**
+    - Form _K_ clusters by assigning each point to its **closest** centroid
+    - Re-compute the centroid (i.e., _**mean point**_) of each cluster
+  - **Until** convergence criterion is satisfied (**e.g., no change of cluster
+    membership, or a certain # of iterations have been reached, or, the [[SSE]]
+    is < a pre-defined threshold**)
+- Different kinds of distance measures can be used
+  - [[Manhattan distance]] ($L_1$ norm), [[Euclidean distance]] ($L_2$ norm),
+    [[Cosine similarity]], [[Mahalanobis distance]] ![[CleanShot 2023-10-24 at
+15.34.07@2x.png]]
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