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, …, CK. Centroids can be used for future associations.
The distortion introduced by the clustering, computed as follows:
This equation denotes the optimization intrinsically done in the K-means algorithm: the centroids are chosen to minimize the intra...
