5 Jan 2026

Clearing Up the Confusion between K-Means and KNN

Mateo Lafalce - Blog

In the world of ML, few acronyms cause as much confusion as K-Means and KNN.

Because both algorithms rely on a variable called K and both involve measuring distances, it is easy to assume they are related. However, they are fundamentally different tools used for completely different tasks.

Supervised vs. Unsupervised

The most important difference lies in the type of learning they perform:

• K-Means is an Unsupervised Learning algorithm. It works with unlabeled data. You give it data without answers, and it tries to find structure on its own.
• KNN is a Supervised Learning algorithm. It works with labeled data. You give it examples with known answers, and it uses them to predict the answer for new data.

K-Means: Clustering: Clustering

Imagine you have a bucket of mixed LEGO bricks, but no instructions and no box. You want to organize them into piles. You don't know what the piles are named; you just know that similar bricks should go together. This is Clustering.

How it Works

K-Means is an iterative algorithm. It tries to partition your dataset into distinct, non-overlapping subgroups.

1. You define K.
2. The algorithm randomly selects K center points.
3. It assigns every data point to the nearest centroid.
4. It moves the centroids to the average position of the points in that cluster.
5. It repeats this process until the centroids stop moving.

What K means here: K represents the number of clusters we want to find.

KNN: Classification:

Imagine you are holding a mystery fruit. To figure out what it is, you place it on a table full of labeled fruits. You look at the fruits sitting immediately next to it. If the three closest fruits are apples, you assume the mystery fruit is also an apple. This is Classification.

The easiest way to remember the difference is to look at your data.

• Do you have the answers already? If yes, and you want to predict a label for a new item, use KNN.
• Do you have a mess of raw data and want to find hidden patterns or segments? Use K-Means.

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