In both industry and business, segmenting data into homogeneous groups is key to making informed decisions. K-means is one of the most widely used classic clustering algorithms, especially useful for customer segmentation. However, its effective use depends on choosing the right number of clusters and understanding how those groups form. This newsletter explains how K-means works, how to select the optimal number of clusters, and how to evaluate the quality of segmentation.
K-means is an unsupervised algorithm that groups data into \(k\) clusters by minimizing the sum of squared distances between each point and its cluster centroid. The process is iterative: initial centroids are chosen, points are assigned to the nearest centroid, centroids are recalculated as the average of their members, and the cycle repeats until improvements become minimal.
Choosing the number of clusters \(k\) is fundamental. The elbow method plots the sum of squared distances for different \(k\) values, seeking the point where improvements stabilize. The silhouette metric is also used, measuring how well-defined and separated clusters are, helping validate the segmentation.
In a business context, K-means allows segmenting customers into groups with similar behaviors or characteristics. This helps understand each group’s dispersion and size, valuable information for further analyses like Kano diagrams or targeted marketing strategies.
In practice, arbitrary selection of \(k\) or random initial centroids can lead to unrepresentative or unstable clusters, making results hard to interpret and strategies difficult to apply.
To implement K-means effectively, start by defining a reasonable range for \(k\) and run the algorithm for each value. Use the elbow method to observe where improvements in the sum of squared distances stabilize, and the silhouette metric to validate cluster quality. Also consider the temporal stability of groups if working with dynamic data.
In manufacturing plants, K-means may be less frequent compared to Deep Learning or temporal analyses, but in business it is a simple and powerful tool to segment customers and understand behavior. This can feed more complex analyses and guide strategic decisions.
Keep in mind that cluster quality depends on proper feature selection and preprocessing. Also, choosing good initial centroids (e.g., with K-means++) can speed convergence and improve results.