K Means Clustering example 30
A focused Machine Learning example for k means clustering with output and explanation.
K Means Clustering example 30
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Input
Terminal
SuccessReady.
Run code to see output here.
What this example teaches
K Means 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 K Means 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 k means clustering without surrounding noise, so you can see the idea clearly.
Where it is used in real projects
K Means 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 K Means Clustering example and run it again.
Advanced variation
Combine K Means Clustering with validation, error handling or reusable structure.