6 Jan 2026

Why K-means and KNN Don't Always Agree

Mateo Lafalce - Blog

It is a common misconception that algorithms using distance metrics will always reach the same conclusion. Specifically, many wonder:

If I give K-means and KNN the same data point, will they predict the same cluster or class?

The short answer is no. While both rely on the distance between points, they see data through fundamentally different lenses.

The primary reason for the discrepancy lies in what the algorithms use as a reference point:

• K-means is about the Middle: When K-means makes a prediction, it looks at the centroid of a cluster. It asks, Which center point is closest to this new data point? This creates rigid, straight-line boundaries known as Voronoi cells.
• KNN is about the Crowd: KNN doesn’t care about a central average. Instead, it looks at the actual data points immediately surrounding the new point. It asks, Who are my k closest neighbors, and what is their majority label?

Imagine a data point sitting on the edge of a cluster. It might be mathematically closer to the center of Cluster A, but it could be physically surrounded by a dense group of individual points belonging to Cluster B.

In this scenario:

1. K-means would assign it to Cluster A because the global center is closer.
2. KNN would assign it to Cluster B because the local neighbors are more prevalent.

K-means assumes clusters are roughly spherical and balanced. KNN, however, is much more flexible and can adapt to wiggly or irregular shapes in data. Because of this, K-means offers a global perspective based on averages, while KNN offers a local perspective based on proximity.

Distance might be the common language, but the way these two algorithms interpret that distance is what sets them apart.

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