Clustering is a fundamental technique in unsupervised machine learning, used to group similar data points. Here's a guide to key algorithms:

1. K-Means Clustering 📊

  • Principle: Partitions data into k clusters based on distance metrics (e.g., Euclidean).
  • Pros: Simple, efficient for large datasets.
  • Cons: Sensitive to initial centroids, requires k to be predefined.
  • Use Cases: Customer segmentation, image compression.
  • K_Means

2. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) 🔍

  • Principle: Groups data based on density, identifying outliers.
  • Pros: No need to predefine k, handles noise well.
  • Cons: Parameters tuning is complex.
  • Use Cases: Anomaly detection, spatial data analysis.
  • DBSCAN

3. Hierarchical Clustering 🌐

  • Principle: Builds a tree of clusters (dendrogram) via agglomerative or divisive methods.
  • Pros: No k required, visualizes relationships.
  • Cons: Computationally expensive for large data.
  • Use Cases: Biological data analysis, document clustering.
  • Hierarchical_Clustering

4. Gaussian Mixture Models (GMM) 📈

  • Principle: Assumes data is a mixture of Gaussian distributions.
  • Pros: Handles non-spherical clusters, probabilistic.
  • Cons: Requires optimization (e.g., EM algorithm).
  • Use Cases: Image segmentation, speech recognition.
  • GMM

For deeper exploration, learn more about clustering techniques. 🚀