Clustering - What is K clustering with example?

Choosing a "k" value of 3, for example, causes the algorithm to pick three centroids at random. Then, K-means will place every data point in the dataset on top of the nearest centroid. This implies that if a data point is closer to the centroid of a certain cluster than any other centroid, then it is likely to be part of that cluster.