Where Is Unsupervised Learning Used?
Unsupervised Learning Is Commonly Used for Finding Meaningful Patterns and Groupings Inherent in Data, Extracting Generative Features, and Exploratory...
Unsupervised learning is commonly used for finding meaningful patterns and groupings inherent in data, extracting generative features, and exploratory purposes.
Where can we use unsupervised learning?
Some use cases for unsupervised learning — more specifically, clustering — include:
- Customer segmentation, or understanding different customer groups around which to build marketing or other business strategies.
- Genetics, for example clustering DNA patterns to analyze evolutionary biology.
What are examples of unsupervised learning?
Below is the list of some popular unsupervised learning algorithms:
- K-means clustering.
- KNN (k-nearest neighbors)
- Hierarchal clustering.
- Anomaly detection.
- Neural Networks.
- Principle Component Analysis.
- Independent Component Analysis.
- Apriori algorithm.
David Miller brings 15 years of experience in global economics, personal finance strategy, and market dynamics. He specializes in turning complex economic trends into actionable insights for everyday readers.
