Unsupervised learning is a type of machine learning where the algorithm learns from unlabeled data. This means that the algorithm is not given any explicit instructions on what to do with the data, and it must find patterns and relationships on its own.
Key Concepts
- Clustering: Grouping data into clusters or classes based on their similarity.
- Dimensionality Reduction: Reducing the number of variables in a dataset while retaining the most important information.
- Association Rules: Finding patterns in data that indicate a relationship between variables.
Common Algorithms
- K-Means Clustering
- Principal Component Analysis (PCA)
- Apriori Algorithm
Resources
For more information on unsupervised learning, check out our Introduction to Machine Learning.
Clustering
Clustering is a powerful technique that can be used to uncover hidden patterns in data. It is often used in market segmentation, image processing, and anomaly detection.
Dimensionality Reduction
Dimensionality reduction is crucial for handling high-dimensional data. PCA is one of the most popular methods for reducing the dimensionality of a dataset.
Association Rules
Association rules are used to find interesting relationships between variables in a dataset. This can be useful for market basket analysis and recommendation systems.
For further reading on unsupervised learning, visit our Machine Learning Documentation.