Clustering algorithm implementation: K-means clustering algorithm
The K-means clustering algorithm is a commonly used unsupervised learning algorithm for dividing a data set into K different categories. In this algorithm, we need to specify the number K of clusters in advance, and then iteratively assign data points to different cluster centers until the convergence condition is reached. This article will introduce the implementation process of the K-means clustering algorithm in detail, and provide the corresponding source code.
First, we need to define a dataset. Assuming we have a two-dimensional dataset with N data points, we can use the following code to create a randomly generated dataset:
#include <iostream>
#include <vector>
#include <random>
struct Point