Unsupervised learning is a type of machine learning where the algorithm learns from unlabeled data. This means that the data does not have any predefined categories or labels. The goal of unsupervised learning is to find patterns and relationships in the data without any prior knowledge.
Key Concepts
- Clustering: Grouping similar data points together.
- Dimensionality Reduction: Reducing the number of variables in a dataset while retaining the essential information.
- Association Rules: Finding interesting relationships between variables in large databases.
Clustering
Clustering is one of the most popular unsupervised learning techniques. It is used to group similar data points together based on their features.
- K-Means Clustering: A simple and widely used clustering algorithm that divides the data into K clusters.
- Hierarchical Clustering: A method that builds a hierarchy of clusters.
Dimensionality Reduction
Dimensionality reduction is used to reduce the number of variables in a dataset while preserving the important information.
- Principal Component Analysis (PCA): A technique used to reduce the dimensionality of a dataset by transforming it into a set of principal components.
- t-SNE: A technique used to visualize high-dimensional data in two or three dimensions.
Association Rules
Association rules are used to find interesting relationships between variables in large datasets.
- Apriori Algorithm: A classic algorithm used to find frequent itemsets in a transaction database.
- Eclat Algorithm: An extension of the Apriori algorithm.
Further Reading
For more information on unsupervised learning, you can read our comprehensive guide on Unsupervised Learning.