Clustering is a type of unsupervised learning in machine learning.

The goal of clustering is to group similar data points together based on their features.

The output of clustering is a set of clusters, each containing a group of similar data points.

Clustering algorithms do not use labeled data, which means they do not require prior knowledge of the ground truth.

Clustering is an exploratory analysis technique that helps to identify hidden patterns or structure in the data.

The most commonly used clustering algorithms include k-means clustering, hierarchical clustering, and DBSCAN.

Clustering algorithms require a distance or similarity measure to determine how close or similar two data points are

The choice of distance measure can affect the quality of the clustering results.