What is a centroid-based clustering?
A category of clustering algorithms that organizes data into nonhierarchical clusters.
centroid-based clustering explained in plain English
A category of clustering algorithms that organizes data into nonhierarchical clusters. k-means is the most widely used centroid-based clustering algorithm. Contrast with hierarchical clustering algorithms. See Clustering algorithms in the Clustering course for more information.
Example
Practitioners refer to centroid-based clustering when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.
People also read
- A/B testing
A statistical way of comparing two (or more) techniques—the A and the B.
- ablation
A technique for evaluating the importance of a feature or component by temporarily removing it from a model.
- accuracy
The number of correct classification predictions divided by the total number of predictions.
- activation function
A function that enables neural networks to learn nonlinear (complex) relationships between features and the label.
- active learning
A training approach in which the algorithm chooses some of the data it learns from.
- adaptation
Synonym for tuning or fine-tuning.
- agglomerative clustering
See hierarchical clustering.
- anomaly detection
The process of identifying outliers.
- area under the PR curve
See PR AUC (Area under the PR Curve).
- area under the ROC curve
See AUC (Area under the ROC curve).