Unsupervised learning is a type of machine learning where the algorithms learn from a dataset without labels. The main goal is to find patterns and relationships within the data without any explicit instruction on what to do.
Key Points of Unsupervised Learning
- Clustering: Grouping similar data points together.
- Dimensionality Reduction: Reducing the number of variables in a dataset while retaining the original information.
- Association: Finding interesting relationships between variables in large datasets.
Clustering
One of the most popular applications of unsupervised learning is clustering. It is used to identify patterns in the data. For example, in e-commerce, clustering can be used to segment customers into groups based on their purchasing behavior.
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Dimensionality Reduction
Dimensionality reduction techniques are used to reduce the number of features in a dataset while preserving as much of the original information as possible. This can be helpful in improving the performance of machine learning models and reducing the complexity of the data.
Example:
Association
Association rules are used to find interesting relationships between variables in large datasets. For example, in a retail store, it may be discovered that customers who buy milk are also likely to buy bread.
Example: