# Gaussian Mixture Models vs K-Means. Which One to Choose? 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.

## How They Work

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

### K-Means

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
2. Initialize the vector μ_k that defines a central point of each cluster
3. Assign each data point *x *to the closest cluster centre
4. Recalculate central points *μ_k *foreach cluster
5. Repeat 3–4 until central points stop moving

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