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.

Examples

• Clustering
• Anomaly Detection
• dimensionality-reduction
• Association Rules