What is Clustering?
Grouping similar items together without predefined categories, discovering natural clusters based on shared characteristics.
Clustering explained in plain English
Clustering groups similar items together without predefined categories. The system discovers natural clusters in data based on shared characteristics.
It is a core technique in unsupervised machine learning.
Analogy
Clustering is like organising a mixed box of photographs into piles of similar scenes — beaches, birthdays, landscapes — without anyone telling you the categories in advance.
Example
A retailer clusters shoppers into behaviour groups to tailor campaigns without manually defining segments upfront.
How is Clustering used?
Common misconceptions about Clustering
Clusters are not always meaningful — algorithms will group data even when no natural structure exists.
People also read
- A/B testing
A method of comparing two versions of a product or service to determine which one performs better
- ablation
A technique used to remove or disable parts of a machine learning model to understand their importance
- accuracy
The degree to which a model's predictions match the actual outcomes
- activation function
A mathematical function that introduces non-linearity into a neural network model
- active learning
A machine learning approach where the model actively selects the most informative data to learn from
- adaptation
The process of adjusting to new or changing conditions
- agglomerative clustering
A type of hierarchical clustering that groups similar data points together
- anomaly detection
The process of identifying data points that do not conform to expected patterns or behaviors
- area under the PR curve
A measure of a model's performance in classification tasks
- area under the ROC curve
A measure of a model's ability to distinguish between positive and negative classes