What Unsupervised Learning Meaning, Applications & Example
A machine learning approach that finds patterns in data without labels.
What is Unsupervised Learning?
Unsupervised Learning is a type of machine learning where the model is trained on data that has no labeled outcomes or target variables. The goal is for the model to identify patterns, structures, or relationships in the data without explicit guidance on what the correct output should be.
Types of Unsupervised Learning
- Clustering : The task of grouping similar data points together based on their features. Common algorithms include K-means and DBSCAN.
- Dimensionality Reduction: Reducing the number of features while retaining as much information as possible. Techniques like PCA (Principal Component Analysis) and t-SNE are often used.
- Anomaly Detection : Identifying data points that are significantly different from the rest of the dataset. This is used in fraud detection or system monitoring.
- Association Rule Learning: Discovering relationships between variables in large datasets, commonly used in market basket analysis.
Applications of Unsupervised Learning
- Customer Segmentation: Clustering customers into groups based on behavior or preferences to tailor marketing strategies.
- Data Compression: Reducing the size of data, such as in image or video compression, using dimensionality reduction techniques.
- Fraud Detection: Identifying unusual patterns or outliers in transaction data to detect fraudulent activity.
- Recommendation Systems: Grouping items or users to make recommendations, like grouping similar movies or products for a user.
Example of Unsupervised Learning
In market basket analysis, unsupervised learning algorithms like association rule learning are used to identify products that are often bought together. For example, if many customers purchase bread and butter together, a retailer may use this information to place these items closer in-store or recommend them together online.