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

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]:

  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

machine-learning clustering towards-data-science k-means

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