Simple Explanation of the K-Means Unsupervised Learning Algorithm

The video tutorial explains the K-means clustering algorithm, starting with an introduction to clustering and the need for computer-based organization of data. It details the steps of the K-means algorithm, including selecting initial clusters, assigning data points, and recalculating means until clusters stabilize. The tutorial discusses optimizing the algorithm by running multiple trials and choosing the best grouping based on variance. It also covers methods for selecting the optimal number of clusters, such as the Elbow method and trial and error.

Explain the process of assigning points to clusters in K means.

What happens to the variance as the value of K increases?