Distribution-based Clustering

Distribution-based clustering is a family of clustering methods that assume your data was generated by a mixture of probability distributions, most often Gaussian (bell-shaped) ones. Each cluster is one distribution, and the further a point sits from a distribution’s centre, the lower its probability of belonging to that cluster. Because membership is a probability, these methods are naturally soft clustering approaches.

How it works

The best-known example is the Gaussian Mixture Model (GMM). You choose how many components (clusters) to fit, and the algorithm, usually expectation-maximisation, repeats two steps:

• Expectation: estimate, for every point, the probability that it came from each distribution.
• Maximisation: update each distribution’s centre, spread and shape to fit the points weighted by those probabilities.

It stops when the estimates settle. Unlike K-means, which assumes round clusters of similar size, a GMM can fit stretched or overlapping clusters of different sizes, and it returns a probability per cluster for each point.

For text, MLforSEO places LDA in this family: it models each document as a mix of topics and each topic as a distribution over words, so a page can be 70% about one topic and 30% about another.

How it’s used in SEO and marketing

• Customer segmentation on numeric data such as purchase frequency and average order value, where customers sit between segments.
• Topic modelling of site content with LDA, when pages genuinely cover several themes.
• Scoring how typical a data point is, for example flagging sessions or pages with a low probability under every cluster as unusual.

Example

A retailer fits a three-component GMM to customers’ order frequency and average order value. One customer comes out 85% “occasional big spender” and 15% “loyal regular”. A hard method would force a single segment; the probabilities let the team include that customer in a cross-sell campaign aimed at both groups.

Limitations and things to watch

• You still have to choose the number of components; information criteria such as BIC help compare options.
• The Gaussian assumption may not fit real data, and results can depend on the starting point.
• GMMs work on numeric features. Text needs to become vectors first, and high-dimensional embeddings usually need dimensionality reduction before a GMM is practical.
• Probabilities are only as meaningful as the features you give the model.

Learn it on MLforSEO

• What Machine Learning Clustering Is, and Where It Fits in SEO
• Topic Modelling for SEO with LDA (Latent Dirichlet Allocation)
• Complete Guide to ML Clustering for Marketing & SEO: Use Cases & Model Choices
• The Introduction to Machine Learning for SEO course, where the clustering module covers the algorithm families