🧠 Clustering is a fundamental unsupervised learning technique used to group similar data points together. It's widely applied in data analysis, pattern recognition, and customer segmentation. Let's explore key algorithms and their use cases!

Common Clustering Algorithms

  1. K-Means Clustering
    📊 A centroid-based algorithm that partitions data into K clusters.

    KMeans Clustering
    *Example:* [Understanding K-Means](/learn/machine-learning/clustering-algorithms/kmeans)

  2. Hierarchical Clustering
    🌐 Builds a tree of clusters, either agglomerative (bottom-up) or divisive (top-down).

    Hierarchical Clustering

  3. DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
    🔍 Identifies clusters based on density, capable of finding arbitrary shapes.

    DBSCAN Clustering

  4. Gaussian Mixture Models (GMM)
    📈 Probabilistic method assuming data points are generated from a mixture of Gaussian distributions.

    GMM Clustering

Applications of Clustering

  • Customer segmentation 🎯
  • Image compression 🖼️
  • Anomaly detection ⚠️
  • Document classification 📄

Key Considerations

  • Choose the right algorithm based on data distribution and cluster shape.
  • Normalize data to avoid bias in distance calculations.
  • Validate results using metrics like silhouette score 📊 or domain knowledge 🧠.

🔗 For deeper insights, check our Machine Learning Foundations Guide.