Hands-On Artificial Intelligence for Cybersecurity

K-Means in depth

We will now deal with the K-Means clustering algorithm in more depth.

As previously stated, K-Means is an unsupervised algorithm, that is, it does not presuppose the prior knowledge of the labels associated with the data.

The algorithm takes its name from the fact that its final purpose is to divide the data into k different subgroups. Being a clustering algorithm, it proceeds to the subdivision of the data into different subgroups on the basis of a chosen measure to represent the distance of the single samples (usually, this measure is the Euclidean distance) from the center of the respective cluster (also known as centroid).

In other words, the K-Means algorithm proceeds to group the data into distinct clusters, minimizing ...