Unsupervised Learning

Unsupervised learning works with unlabeled data, meaning the algorithm must find patterns or structures in the data without any explicit guidance on what the output should be. This approach is typically used for clustering, anomaly detection, and association tasks.

Unsupervised Learning

Algorithms that work with unlabeled data to identify structures and hidden patterns.

• TASK (Clustering):

  • k-Means

  • Hierarchical Clustering

  • DBSCAN (Density-Based Spatial Clustering of Applications with Noise)

  • Gaussian Mixture Models (GMM)

• TASK (Dimensionality Reduction):

  • Principal Component Analysis (PCA)

  • Linear Discriminant Analysis (LDA)

  • t-SNE (t-distributed Stochastic Neighbor Embedding)

  • Autoencoders

• TASK (Search and Optimization)

  • Heuristic Methods

    Methods that use approximations to find solutions for complex problems.

    • Genetic Algorithms

    • Ant Colony Optimization

    • Particle Swarm Optimization