Discovering K-Means clustering
In this section, we'll understand what unsupervised learning is and we'll learn the basics of the K-Means clustering technique.
K-Means is an unsupervised learning algorithm that solves clustering problems. This technique is used to classify data into a set of classes. The letter k represents the number of clusters that are fixed a priori. For our business scenario, we'll use three different clusters.
Important note
While supervised learning is based on a prior knowledge of what the output values of labels should be in a training dataset, unsupervised learning does not leverage labeled datasets. Its goal is to infer the structure of data within a training dataset, without any prior knowledge of it.
Each cluster of data is characterized by a centroid. The centroid represents the midpoint of the cluster and is identified during the training stage and according to the features of the model.
After the training of the K-Means...
