Clustering example 61
A focused Machine Learning example for clustering with output and explanation.
Clustering example 61
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Input
Terminal
SuccessReady.
Run code to see output here.
What this example teaches
Clustering
Output
The experiment prepares features, labels, metrics and validation rows, trains or scores a small model pattern, and prints a metric you can compare.
Line-by-line explanation
- Line 1 sets up the Clustering example: points = [2, 3, 10, 11, 12, 25].
- Line 2 adds one required part of the working pattern: centers = [3, 11].
- Line 3 adds one required part of the working pattern: clusters = {center: [] for center in centers}.
- Line 4 adds one required part of the working pattern: for point in points:.
- Line 5 adds one required part of the working pattern: nearest = min(centers, key=lambda center: abs(point - center)).
- Line 6 adds one required part of the working pattern: clusters[nearest].append(point).
Why this example is useful
This example is useful because it isolates clustering without surrounding noise, so you can see the idea clearly.
Where it is used in real projects
Clustering appears in real Machine Learning work when a feature needs a clear pattern that can be reviewed and changed safely.
Beginner variation
Change one label, value or condition in the Clustering example and run it again.
Advanced variation
Combine Clustering with validation, error handling or reusable structure.