What is clustering?

02 · See it in action

Follow the steps

1. Choose numeric features for a synthetic collection.
2. Scale features when their units differ substantially.
3. Try a small range of cluster counts.
4. Inspect representative examples and ambiguous boundary cases.

Worked example

from sklearn.cluster import KMeans
x = [[0, 0], [0, 1], [9, 9], [9, 8]]
m = KMeans(n_clusters=2, random_state=7, n_init=10).fit(x)
print(m.labels_)
# Group IDs are arbitrary; nearby points should share a group.

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