Clustering Mixed Data with K-Means: From FAMD to Segments

Georgios Kokkinopoulos

11 min readMay 13, 2026

Applying K-Means to FAMD-transformed data, selecting the optimal number of clusters and evaluating the final segmentation.

Press enter or click to view image in full size Photo by Jeyakumaran Mayooresan on Unsplash

Introduction

In the second part of the series (Applying FAMD in Python — Preparing Mixed Data for Clustering— full list of the project here: Customer Segmentation with Mixed Data), we transformed our dataset using Factor Analysis of Mixed Data (FAMD) to create a meaningful lower-dimensional representation of mixed variables.

In this article, we take the next step and apply K-Means clustering to this transformed dataset in order to identify distinct groups of customers .

We will focus on the technical aspects of clustering, including how to determine the optimal number of clusters and how to fit the model .

The resulting clusters will form the foundation for the insights and business…