Gaussian Mixture Models (GMM)

A Gaussian Mixture Model (GMM) is an unsupervised clustering model that assumes your data was generated by several overlapping bell-shaped (Gaussian) distributions. It estimates where each distribution sits and how it is shaped, then gives every data point a probability of belonging to each cluster. In MLforSEO’s clustering framework it is the main example of distribution-based clustering for numeric data.

How it works

You choose the number of components (clusters). The model then repeats two steps, known as the expectation-maximisation (EM) algorithm:

• Expectation: given the current guesses, calculate the probability that each point belongs to each component.
• Maximisation: update each component’s centre, spread and shape (its covariance) and its weight, using those probabilities.

It stops when the estimates stop changing. Because each cluster has its own covariance, GMM can fit stretched or tilted oval clusters of different sizes, where K-means assumes roughly round, similar-sized ones. And because membership is a probability, it is a form of soft clustering: a point can be 85% in one cluster and 15% in another. You can still take the highest probability as a hard label.

How it’s used in SEO and marketing

• Customer segmentation on numeric behaviour such as order frequency, average order value and recency.
• Grouping pages by performance metrics (clicks, impressions, CTR, position) where the groups overlap.
• Flagging unusual data: points with low probability under every component are candidates for anomalies.
• Image analysis, such as segmenting colours in product photos.

Example

MLforSEO’s clustering post uses a customer born in 1995. With hard clustering they are either Gen Z or Millennial. With a soft approach like GMM, the same customer might come out 85% Gen Z and 15% Millennial based on their actual behaviour, so you can target them with the right mix of messages.

Limitations and things to watch

• You still choose the number of components; compare models with a criterion such as BIC to help.
• It assumes each cluster is roughly Gaussian; skewed metrics such as revenue usually need a log transform first.
• Results depend on the starting point, so run several initialisations.
• It is built for numeric data. For text, the distribution-based equivalent MLforSEO points to is LDA.
• Scale your features first so one metric does not dominate.

Learn it on MLforSEO

• What Machine Learning Clustering Is, and Where It Fits in SEO
• Customer Segmentation with Machine Learning Using Three Clustering Approaches
• Complete Guide to ML Clustering for Marketing & SEO
• Introduction to Machine Learning for SEO course