k-Means Clustering is a type of Hard Clustering that aims to partition 𝑛 observations into 𝑘 clusters

We note ${𝑐}^{\left(𝑖\right)}$ the cluster of data point $𝑖$ and ${𝜇}_{𝑗}$ the center of cluster $𝑗$

Algorithm ― After randomly initializing the cluster centroids ${𝜇}_1,{𝜇}_2,...,{𝜇}_{𝑘}\in {\mathrm{ℝ𝑛}}^{}$

K-Means - General Algorithm

iterative-clustering-algorithm(points, k) {
	cluster-centers = k random points (means)
	do until convergence:
		for each point in points:
		assign point to closest cluster-center
		change each cluster-center to the average of its assigned points
}

K-Means - Other

• Louvain Method vs K-Means
• K-Means Clustering vs Fuzzy C-Means Clustering