Unsupervised Learning
What is Unsupervised Learning?
Unsupervised learning is a subset of artificial intelligence that deals with the identification of patterns in data without pre-existing labels. Unlike supervised learning, where the model is trained on a labeled dataset, unsupervised learning algorithms work with data that has no labels, and the goal is to infer the natural structure present within a set of data points. This approach is particularly useful in scenarios where the cost of labeling data is prohibitive or where the structure of the data is unknown. Techniques commonly used in unsupervised learning include clustering, where data points are grouped into clusters based on similarity, and association, where the goal is to identify rules that describe large portions of data. Unsupervised learning is widely used in fields such as customer segmentation, anomaly detection, and market basket analysis.
A type of machine learning where the algorithm is trained on unlabeled data and must find patterns and relationships within the data without explicit instructions.
Examples
- Customer Segmentation: Retail companies often use unsupervised learning to group customers into different segments based on purchasing behavior. This helps in targeting marketing efforts and improving customer satisfaction.
- Anomaly Detection: Financial institutions use unsupervised learning to detect fraudulent transactions. By identifying patterns that deviate from the norm, these algorithms can flag potentially fraudulent activity for further investigation.
Additional Information
- Unsupervised learning can be more challenging than supervised learning because there are no labels to guide the learning process.
- Common algorithms used in unsupervised learning include K-means clustering, hierarchical clustering, and principal component analysis (PCA).