What is clustering?

Clustering is an unsupervised method that groups unlabeled data by similarity.

What is clustering?

Clustering is unsupervised learning that splits similar examples into groups without labels. Uses: customer segmentation, document grouping, anomaly detection, region separation in images.

from sklearn.cluster import KMeans
km = KMeans(n_clusters=3, n_init=10, random_state=0).fit(X)
print(km.labels_[:10])   # cluster number of each example

Cluster numbers are arbitrary: cluster 0 isn't the "first" or the "best", just a name.

Similarity and scale

Most clustering algorithms rely on distance, so scaling is a must. Also, the definition of a "good cluster" changes with the algorithm:

KMeans: round clusters of similar size.
DBSCAN: density-based; any shape, separates noise.
Hierarchical: a tree of nested clusters.

from sklearn.preprocessing import StandardScaler
Xs = StandardScaler().fit_transform(X)
km = KMeans(n_clusters=4, n_init=10, random_state=0).fit(Xs)

Exercise First clustering

Train a KMeans(n_clusters=4, n_init=10, random_state=0) on the synthetic 4-centre data. Print how many examples each cluster contains with np.bincount(km.labels_).

Show example solution

import numpy as np
from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans

X, y_gercek = make_blobs(n_samples=300, centers=4, cluster_std=0.8, random_state=0)

km = KMeans(n_clusters=4, n_init=10, random_state=0).fit(X)
print(np.bincount(km.labels_))

Test yourself

How does clustering differ from classification?
- There is no difference
- It uses no labels in training
- It is more accurate
- It finds only two groups
Show answer

It uses no labels in training — Groups are derived from the data's own structure; no correct answer is given.
Read the cluster label of every example.
```
etiket = km.____
```
Show answer
```
etiket = km.labels_
```
Match the algorithms.

KMeans

Round clusters
DBSCAN

Density, noise
Hierarchical

Cluster tree
StandardScaler

Required before distance

Next lesson: KMeans

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