Advanced Topics in Unsupervised Learning

Duration: 7 min

This module delves into advanced techniques in unsupervised learning, including K-Means, DBSCAN, Hierarchical Clustering, PCA, t-SNE, and Autoencoders. Understanding these methods is crucial for data scientists and machine learning practitioners aiming to uncover hidden patterns and structures within complex datasets without the need for labeled data.

K-Means Clustering

K-Means is a popular unsupervised learning algorithm used for clustering. It aims to partition the data into K distinct clusters where each data point belongs to the cluster with the nearest mean. The algorithm iteratively assigns data points to clusters and updates the cluster centroids until convergence. It is widely used in market segmentation, image compression, and anomaly detection.

from sklearn.cluster import KMeans
import numpy as np

# Generate synthetic data
X = np.array([[1, 2], [1, 4], [1, 0],
              [4, 2], [4, 4], [4, 0]])

# Apply K-Means clustering
kmeans = KMeans(n_clusters=2, random_state=0).fit(X)

# Get cluster labels and centroids
labels = kmeans.labels_
centroids = kmeans.cluster_centers_

print('Cluster labels:', labels)
print('Cluster centroids:', centroids)

Try it in Google Colab:

Cluster labels: [0 0 0 1 1 1]
Cluster centroids: [[1. 2.]
 [4. 2.]]

DBSCAN Clustering

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm. Unlike K-Means, DBSCAN does not require specifying the number of clusters beforehand. It forms clusters based on the density of data points, effectively identifying clusters of varying shapes and sizes and marking outliers as noise. DBSCAN is particularly useful in spatial data analysis and anomaly detection.

from sklearn.cluster import DBSCAN
import numpy as np

# Generate synthetic data
X = np.array([[1, 2], [2, 2], [2, 3],
              [8, 7], [8, 8], [25, 80]])

# Apply DBSCAN clustering
dbscan = DBSCAN(eps=3, min_samples=2).fit(X)

# Get cluster labels
labels = dbscan.labels_

print('Cluster labels:', labels)

💡 Tip: When using DBSCAN, carefully choose the eps (maximum distance between two samples) and min_samples (minimum number of samples in a neighborhood for a point to be considered as a core point) parameters to achieve the desired clustering results.