K-Means is a popular unsupervised learning algorithm used to group data points into clusters based on similarity.
Unlike classification, it doesn’t use labels. Instead, it identifies patterns or natural groupings in data automatically.
It’s like sorting a pile of mixed fruits into separate baskets based on color and size.
How K-Means Works
- Choose the number of clusters K.
- Randomly initialize centroids for each cluster.
- Assign each data point to the nearest centroid.
- Recalculate centroids based on the points in each cluster.
- Repeat steps 3–4 until centroids stabilize (no significant changes).
Advantages of K-Means
- Simple and easy to implement
- Scales well to large datasets
- Works well for discovering hidden patterns
- Efficient and fast in practice
Disadvantages
- Requires choosing K in advance
- Sensitive to outliers and noise
- Assumes clusters are spherical and equally sized
- Can converge to local minima
Real-World Examples
- Customer segmentation for marketing
- Document clustering in NLP
- Image compression
- Anomaly detection
- Grouping similar products or items
Conclusion
K-Means is a straightforward and effective clustering technique. It’s widely used for exploratory data analysis and pattern discovery in unlabeled datasets.
Citations
https://savanka.com/category/learn/ai-and-ml/
https://www.w3schools.com/ai/