Summary - KMeans
- Machine Learning course
- Intermediate
- Lesson 58/112
- 6 min
In this group you learned KMeans parameters and properties.
Summary: KMeans
In this group:
- Lloyd's algorithm: assignment + update; inertia never rises but can get stuck in a local optimum.
- k-means++ initialises with spread-out centres;
n_initpicks the best of several starts. inertia_depends on scale and always falls with k; the silhouette, Calinski-Harabasz and Davies-Bouldin complement it.random_stategives reproducibility; ARI across seeds measures stability.MiniBatchKMeansfor large data.
Practice KMeans and MiniBatch
Train KMeans(n_clusters=5, n_init=10, random_state=0) and MiniBatchKMeans(n_clusters=5, batch_size=100, n_init=3, random_state=0) on the same data. Print the two inertias as integers with round on one line, then the ARI of the two labellings with round(..., 3).
Starter code
import numpy as np
from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans
X, _ = make_blobs(n_samples=500, centers=5, cluster_std=1.0, random_state=3)
from sklearn.cluster import MiniBatchKMeans
from sklearn.metrics import adjusted_rand_score
# buraya yazSample solution
import numpy as np
from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans
X, _ = make_blobs(n_samples=500, centers=5, cluster_std=1.0, random_state=3)
from sklearn.cluster import MiniBatchKMeans
from sklearn.metrics import adjusted_rand_score
km = KMeans(n_clusters=5, n_init=10, random_state=0).fit(X)
mb = MiniBatchKMeans(n_clusters=5, batch_size=100, n_init=3, random_state=0).fit(X)
print(round(km.inertia_), round(mb.inertia_))
print(round(adjusted_rand_score(km.labels_, mb.labels_), 3))Expected output
1000 1002 1.0
Questions and answers
Review the lesson questions together with their answers. To test yourself, solve the lesson interactively.
1 If KMeans results vary a lot from seed to seed, what should be tried first?
- Deleting random_state
- Increasing n_init and reviewing k Correct answer
- Setting max_iter to 1
- Removing scaling
Explanation: More starts reduce local optima; instability can also point to a wrong k.
2 Use mini-batch KMeans for large data.
Question
mb = ____(n_clusters=5, batch_size=256)
Answer
mb = MiniBatchKMeans(n_clusters=5, batch_size=256)Explanation: MiniBatchKMeans uses only a batch of data in each round.
3 Match the concepts.
| Term | Meaning |
|---|---|
Lloyd |
Assignment and update |
k-means++ |
Spread-out start |
n_init |
Choosing the best start |
ARI across seeds |
Stability |