Advantages
- Machine Learning course
- Intermediate
- Lesson 63/112
- 5 min
Hierarchical clustering shows cluster structure without specifying k in advance.
Advantages of hierarchical clustering
Hierarchical clustering stands out when:
- k is unknown: the dendrogram shows the structure at different levels together.
- A nested structure is meaningful: taxonomies like species-genus-family, product categories.
- Small to medium data: it works comfortably up to a few thousand examples.
- It is deterministic: there is no random initialisation; every run gives the same result.
Its drawbacks: slowness on large data and not being applicable to new examples.
A classifier on the cluster labels
To assign new examples to clusters, a common pattern is training a classifier on the labels found by hierarchical clustering:
etiket = AgglomerativeClustering(n_clusters=3).fit_predict(X)
from sklearn.neighbors import KNeighborsClassifier
atayici = KNeighborsClassifier(n_neighbors=5).fit(X, etiket)
print(atayici.predict(X_yeni))That way the clustering is done once and later assignments are fast and consistent.
Practice Carry the clustering to new data
Find the labels on the synthetic data with AgglomerativeClustering(n_clusters=3) and train a KNeighborsClassifier(n_neighbors=5) on them. Print with (... == ...).all() that the assigner's predictions for X[:5] match the original labels. Then run the same clustering again and print that the labels didn't change.
Starter code
from sklearn.datasets import make_blobs
from sklearn.cluster import AgglomerativeClustering
from sklearn.metrics import adjusted_rand_score
X, y = make_blobs(n_samples=300, centers=3, cluster_std=[0.5, 1.5, 0.8], random_state=4)
from sklearn.neighbors import KNeighborsClassifier
# buraya yazSample solution
from sklearn.datasets import make_blobs
from sklearn.cluster import AgglomerativeClustering
from sklearn.metrics import adjusted_rand_score
X, y = make_blobs(n_samples=300, centers=3, cluster_std=[0.5, 1.5, 0.8], random_state=4)
from sklearn.neighbors import KNeighborsClassifier
etiket = AgglomerativeClustering(n_clusters=3).fit_predict(X)
atayici = KNeighborsClassifier(n_neighbors=5).fit(X, etiket)
print((atayici.predict(X[:5]) == etiket[:5]).all())
print((AgglomerativeClustering(n_clusters=3).fit_predict(X) == etiket).all())Expected output
True True
Questions and answers
Review the lesson questions together with their answers. To test yourself, solve the lesson interactively.
1 Why is random_state not needed in hierarchical clustering?
- The algorithm is deterministic Correct answer
- There are always 2 clusters
- The data isn't shuffled
- It is needed
Explanation: At every step the closest pair is chosen; there is no randomness.
2 Train an assigner on the cluster labels.
atayici = KNeighborsClassifier(n_neighbors=5).fit(X, ____)atayici = KNeighborsClassifier(n_neighbors=5).fit(X, etiket)Explanation: The cluster labels become the classifier's target.
3 Match the situations.
| Term | Meaning |
|---|---|
k unknown |
Dendrogram |
Nested categories |
Hierarchy |
Millions of samples |
MiniBatchKMeans |
Assigning a new sample |
KNN on labels |