Knowledge Graph — Coursera Notes › Academic disciplines › Computer Science / Information Technology › Artificial Intelligence › Machine Learning & Data › Unsupervised Learning

Clustering

concept · part of Unsupervised Learning

A technique that groups similar data points together, including methods like k-means, DBSCAN, and hierarchical clustering.

Inside Clustering (6)

• Davies–Bouldin index — A metric that computes the average similarity ratio of each cluster to its most similar cluster; lower values indicate better clustering.
• DBSCAN — A density-based clustering algorithm that groups close points and identifies outliers, handling varying shapes and densities.
• Elbow method — A method to determine the optimal number of clusters by plotting within-cluster sum of squares (WCSS) and finding the 'elbow' point.
• Gaussian Mixture Models (GMMs) — Probabilistic models that assume data is generated from a mixture of Gaussian distributions, providing soft clustering with probabilities.
• k-means — A clustering algorithm that partitions data into a predefined number of clusters by minimizing distance to centroids.
• Silhouette score — A metric that measures how similar a point is to its own cluster versus others; higher values indicate better-separated clusters.

Connections

• Alternative to DBSCAN
• Alternative to Gaussian Mixture Models (GMMs)
• Uses Silhouette score
• Uses Elbow method
• Uses Davies–Bouldin index

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