Machine learning utilizes unsupervised learning as a method for data processing. This form of learning enables systems to identify and analyze unknown data without outside intervention. Unsupervised learning can recognize patterns that may be missed by manual inspection and can process large volumes of data that would otherwise be beyond human capacity.
Applications of Unsupervised Learning
Here are five common applications of unsupervised learning:
- Clustering involves grouping similar objects based on shared characteristics, typically used for market segmentation, image segmentation, and customer segmentation.
- Anomaly detection utilizes to identify unusual patterns or anomalies in data, frequently deployed in fraud detection, cybersecurity, and fault detection in industrial systems.
- Dimensionality Reduction is applied to reduce the number of features or variables in a dataset while preserving as much relevant information as possible. This is used for simplifying data visualization, minimizing storage requirements, and boosting model effectiveness.
- Generative models generate new data similar to the existing input data, often seen in image generation, music production, and language generation.
- Association rule Learning enables the identification of patterns in data through frequent item sets or rules which link together multiple factors, primarily employed in market basket analysis, recommender systems, and cross-selling.
