Application Area | Examples |
---|---|
Natural Language Processing | Google News article categorization, topic modeling 1 |
Image and Video Analysis | Object recognition, visual perception tasks 1 |
Anomaly Detection | Identifying unusual patterns in data, fraud detection 12 |
Customer Segmentation | Creating buyer personas, understanding customer traits 1 |
Recommendation Systems | Cross-selling strategies, personalized product suggestions 12 |
Marketing | Clustering customer data for targeted campaigns 3 |
Financial Services | Detecting fraudulent transactions, credit scoring 2 |
Healthcare | Medical image analysis, patient clustering 4 |
Pros | Cons |
---|---|
Discovers hidden patterns in data without human guidance12 | Results can be less accurate or predictable23 |
Works with unlabeled data, which is often more abundant and cheaper to obtain12 | Interpretation of results may require domain expertise23 |
Can handle complex, high-dimensional data1 | Prone to overfitting on spurious patterns3 |
Useful for exploratory data analysis and feature learning13 | Difficult to evaluate model performance objectively34 |
Enables analysis of data that humans might easily miss due to bias or complexity1 | May require more computational resources and processing time5 |
Can lead to new business insights or opportunities2 | Results may not always be directly applicable or useful2 |