Unsupervised Learning
- Unsupervised learning uses unlabeled data for training.
- This is particularly useful in problems where it is difficult to obtain labeled data.
- Unsupervised learning algorithms are useful for discovering patterns in data.
- The goal of unsupervised learning is use a feature vector as input and either outputs a feature vector or a label/value.
Use Cases
- Clustering algorithm takes a feature vector as input and outputs a label.
- Dimensionality reduction algorithms take a feature vector as input and output a feature vector with less features.
- Anomaly detection algorithms take a feature vector as input and output is a real number indicating the degree of anomaly.
Examples
- Clustering:
- Anomaly Detection:
- Dimensionality Reduction:
- Association Rule Learning: