Jupyter Notebook showing clustering with K-means algorithm.
The KMeans algorithm clusters data by trying to separate samples in n groups of equal variance, minimizing a criterion known as the inertia or within-cluster sum-of-squares (see below). This algorithm requires the number of clusters to be specified.
This repo has a demonstration of k-means on the 2-dimentional array. Data is provided from .csv file.