Clustering – K-means
K-means is an unsupervised algorithm that creates K disjoint clusters of points with equal variance, minimizing the distortion (also named inertia).
Given only one parameter K, representing the number of clusters to be created, the K-means algorithm creates K sets of points S1, S2, …, SK, each of them represented by its centroid: C1, C2, …, CK. The generic centroid, Ci, is simply the mean of the samples of the points associated to the cluster Si in order to minimize the intra-cluster distance. The outputs of the system are as follows:
- The composition of the clusters S1, S2, …, SK, that is, the set of points composing the training set that are associated to the cluster number 1, 2, …, K.
- The centroids of each cluster, C1, C2