1. Definition
Clustering is an unsupervised learning technique used to group similar data points into clusters, where:
- Points in the same cluster are more similar to each other.
- Points in different clusters are more dissimilar.
👉 It answers: “Which items are similar?” or “How can we group this data?”
2. Key Characteristics
- No labeled output (unlike supervised learning).
- Groups are discovered automatically based on patterns.
- The number of clusters may be pre-defined or learned.
3. Popular Clustering Algorithms
- K-Means Clustering
- Partitions data into k clusters.
- Simple, fast, widely used.
- Hierarchical Clustering
- Builds a hierarchy/tree of clusters.
- Can be agglomerative (bottom-up) or divisive (top-down).
- DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
- Groups data points based on density.
- Handles outliers well.
- Gaussian Mixture Models (GMM)
- Uses probability distributions to form soft clusters.
- Mean Shift Clustering
- Finds clusters by locating dense regions in data.
4. Evaluation Metrics
Since there are no labels, clustering quality is measured differently:
- Silhouette Score – Measures how well a point fits within its cluster.
- Davies–Bouldin Index – Lower values = better separation.
- Inertia (Within-cluster Sum of Squares) – Used in K-Means.
5. Applications
- 🛒 Customer Segmentation – Group customers by buying patterns.
- 🌐 Search Engines – Cluster web pages/documents for relevance.
- 🧬 Bioinformatics – Group genes or proteins with similar functions.
- 📸 Image Segmentation – Divide images into meaningful regions.
- 📰 Topic Modeling – Cluster news articles or social media posts.
- 📊 Market Research – Identify hidden groups in survey responses.
6. Advantages
✅ Works without labeled data.
✅ Reveals hidden patterns.
✅ Helps in exploratory data analysis (EDA).
7. Challenges
⚠️ Choosing the right number of clusters (k) is tricky.
⚠️ Sensitive to noise and outliers.
⚠️ Different algorithms may give different results.
⚠️ High-dimensional data makes clustering harder.
✅ In short: Clustering = Grouping similar data points together without labels.