Unsupervised Learning
In unsupervised learning, the training data is unlabelled.
Typical unsupervised learning tasks include
- clustering,
- visualization,
- dimensionality reduction / feature extraction,
- anomaly detection and
- association rule learning.
Some of the most important unsupervised learning algorithms include
- clustering
- k-means
- hierarchical cluster analysis (HCA)
- expectation maximization
- visualization and dimensionality reduction
- principle component analysis (PCA)
- kernel PCA
- locally-linear embedding (LLE)
- t-distributed stochastic neighbor embedding (t-SNE)
- association rule learning
- Apriori
- Eclat