In this article, we will see that both models offer a different performance in terms of speed and robustness. We will also see that it is possible to use K-Means as an initializer for GMs which tends to boost the performance of the clustering model.

K-Means and Gaussian Mixtures (GMs) are both clustering models. Many data scientist, however, tend to choose a more popular K-Means algorithm. Even if GMs can prove superior in certain clustering problems.

In this article, we will see that both models offer a different performance in terms of speed and robustness. We will also see that it is possible to use K-Means as an initializer for GMs which tends to boost the performance of the clustering model.

First, let’s review the theoretical part of these algorithms. It will help us to understand their behaviour later in the article.

K-Means is a popular non-probabilistic clustering algorithm. The goal of the algorithm is to minimize the distortion measure *J***. **We achieve that by the following iterative procedure [1]:

- Choose the number of clusters
*K* - Initialize the vector
**μ_k**that defines a central point of each cluster - Assign each data point *
*x **to the closest cluster centre - Recalculate central points *
*μ_k **foreach cluster - Repeat 3–4 until central points stop moving

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

Learning is a new fun in the field of Machine Learning and Data Science. In this article, we’ll be discussing 15 machine learning and data science projects.

Basics of Machine Learning: K-Means Clustering. As we dive into the world of “Unsupervised” Machine Learning, we will encounter problems that would require us to cluster the data available to us.

Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant

“How’d you get started with machine learning and data science?”: I trained my first model in 2017 on my friend's lounge room floor.