Clustering
2024 | AI Dictionary
What is Clustering: An unsupervised learning technique that groups similar data points together to discover natural patterns without labeled data.
What is Clustering?
Clustering is an unsupervised learning technique in machine learning that groups similar data points together into clusters. Unlike classification , clustering does not require labeled data, allowing the model to discover natural groupings in the data based on features and similarities.
Types of Clustering
- K-Means Clustering : Divides data into a specified number of clusters (k) by minimizing variance within each cluster.
- Hierarchical Clustering: Builds a hierarchy of clusters, which can be visualized in a tree-like structure called a dendrogram.
- DBSCAN (Density-Based Spatial Clustering of Applications with Noise): Groups data based on density, allowing it to detect clusters of varying shapes and sizes.
Applications of Clustering
- Customer Segmentation: Groups customers based on purchasing behavior, helping businesses tailor marketing strategies.
- Image Segmentation: Divides images into meaningful parts, useful in medical imaging and object detection .
- Anomaly Detection : Identifies outliers or unusual data points, which may indicate fraud or equipment failure.
Example of Clustering
An example of Clustering is in market segmentation, where a retailer groups customers based on their purchasing patterns to target specific demographics with customized promotions and recommendations.
Did you liked the Clustering gist?
Learn about 250+ need-to-know artificial intelligence terms in the AI Dictionary.