In unsupervised learning, as you might guess, the training data is unlabeled. The system tries to learn without a teacher.
Here are some of the most important unsupervised learning algorithms:
- Clustering
- K-Means
- DBSCAN
- Hierarchical Cluster Analysis (HCA)
- Anomaly detection and novelty detection
- One-class SVM
- Isolation Forest
- Visualization and dimensionality reduction
- Principal Component Analysis (PCA)
- Kernel PCA
- Locally Linear Embedding (LLE)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
- Association rule learning
- Apriori
- Eclat