K-Means Clustering: How It Works & Finding The Optimum Number Of Clusters In The Data
Mathematical formulation, Finding the optimum number of clusters and a working example in Python
Introduction
K-means is one of the most widely used unsupervised clustering methods.
The K-means algorithm clusters the data at hand by trying to separate samples into K groups of equal variance, minimizing a criterion known as the inertia or within-cluster sum-of-squares. This algorithm requires the number of clusters to be specified. It scales well to large number of samples and has been used across a large range of application areas in many different fields.
The k-means algorithm divides a set of N samples (stored in a data matrix X) into K disjoint clusters C, each described by the mean μj of the samples in the cluster. The means are commonly called the cluster “centroids”.
K-means algorithm falls into the family of unsupervised machine learning algorithms/methods. For this family of models, the research needs to have at hand a dataset with some observations without the need of having also the labels/classes of the observations. Unsupervised learning studies how systems can infer a…
