Python for Machine Learning - The Complete Beginners Course - Elbow Method

The video tutorial discusses the care means clustering algorithm, emphasizing the importance of forming efficient clusters and the critical task of choosing the optimal number of clusters. It introduces the elbow method as a popular technique for determining the optimal number of clusters. The tutorial explains the calculation of distances between data points and centroids within clusters, using methods like Euclidean and Manhattan distances.

How do you measure the distance between data points and centroids in clustering?

What are some methods that can be used to measure distance in clustering algorithms?