Lecture 11 - K Means Clustering

What is K-Means Clustering?

Is a unsupervised learning algorithm used to partition a dataset into “k” distinct, non-overlapping groups or clusters based on similarities within the data

What is a clustering type of algorithm and its purpose?

Algorithms that group objects or data points into clusters based on their similarities. The goal is to ensure that data points within the same cluster are more similar to each other than those in different clusters. Tries to find inherent patterns or structures in the data

What is a Centroid?

Is the center or “mean” position of a group of points in a dataset. Represents the center of a cluster and is calculated as the average of all the data points within the cluster

How are points assigned to a cluster?

A point is assigned to a cluster it is closest to. This is done by finding the distance from the point to each centroid and selecting the cluster with the smallest distance (closest to the point)