Unsupervised learning is a type of machine learning where the algorithm learns from unlabeled data. This type of learning is particularly useful for finding patterns in large datasets and can be used for various applications such as clustering, anomaly detection, and dimensionality reduction.
Key Concepts
- Clustering: Grouping data into clusters based on similarity.
- Anomaly Detection: Identifying unusual patterns that do not conform to expected behavior.
- Dimensionality Reduction: Reducing the number of variables in a dataset.
Algorithms
- K-Means Clustering
- Hierarchical Clustering
- DBSCAN
- Principal Component Analysis (PCA)
K-Means Clustering
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