Unsupervised learning algorithms are a type of machine learning algorithm that does not require labeled data for training. Instead, they try to find patterns and structure in the data on their own, without any prior knowledge or guidance. Here are some examples of unsupervised learning algorithms:
- Clustering: Clustering is the process of grouping similar data points together into clusters. Unsupervised clustering algorithms include k-means, hierarchical clustering, and density-based clustering.
- Dimensionality reduction: Dimensionality reduction is the process of reducing the number of features in a dataset while retaining as much information as possible. Principal Component Analysis (PCA), t-SNE, and Autoencoders are examples of unsupervised dimensionality reduction algorithms.
- Anomaly detection: Anomaly detection is the process of identifying data points that are significantly different from the majority of the data. Unsupervised anomaly detection algorithms include density-based methods, clustering-based methods, and reconstruction-based methods.
- Generative models: Generative models learn to generate new data that is similar to the training data. Examples of unsupervised generative models include Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), and Boltzmann Machines.
- Association rule mining: Association rule mining is the process of discovering relationships or associations between items in a dataset. Unsupervised association rule mining algorithms include Apriori, FP-Growth, and Eclat.
Unsupervised learning algorithms have numerous applications, including data exploration, clustering and segmentation, anomaly detection, dimensionality reduction, and more.