Financial Risk Manager Part 1

The K-means algorithm separates a sample into clusters by minimizing the sum of squared distances between data points and the centroid of the cluster to which they belong. The algorithm begins by randomly selecting K initial cluster centers and then assigns each point in the sample to the cluster with the nearest center. The algorithm then recalculates the cluster centers as the mean of all the points in the cluster and repeats the process until the cluster assignments do not change or a maximum number of iterations is reached. The goal of the algorithm is to minimize the sum of the squared distances between each point and its assigned cluster center, which results in clusters with points that are more similar to each other.