Unsupervised learning

Unsupervised learning is a type of machine learning that uses algorithms to analyze and cluster unlabeled datasets. These algorithms discover hidden patterns or data groupings without the need for human intervention.

Overview

Unsupervised learning is a type of machine learning that trains itself using data that has not been classified, labeled or categorized. Instead of responding to feedback, unsupervised learning identifies commonalities in the data and reacts based on the presence or absence of such commonalities in each new piece of data.

Types of Unsupervised Learning

There are two main methods of unsupervised learning: clustering and dimensionality reduction.

Clustering

Clustering is a method of unsupervised learning where the model discovers and analyzes a dataset to group (or cluster) them into clusters based on similarity or common patterns. The most common clustering algorithms include K-means, hierarchical, and DBSCAN.

Dimensionality Reduction

Dimensionality reduction is a method that reduces the number of random variables under consideration by obtaining a set of principal variables. It is used to simplify data processing without losing much information. Common dimensionality reduction algorithms include Principal Component Analysis (PCA) and autoencoders.

Applications of Unsupervised Learning

Unsupervised learning has numerous applications, including:

• Anomaly detection: Unsupervised learning can be used to identify unusual data points in your dataset. This is useful in many domains, such as fraud detection, fault detection, and system health monitoring.
• Association rule learning: This is a method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using measures of interestingness.
• Natural language processing: Unsupervised learning is used in natural language processing to extract statistically relevant patterns in data, which are then used to understand natural language.

See Also

• Supervised learning
• Reinforcement learning
• Semi-supervised learning
• Deep learning

References

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