Intermediate · Research
Centroid-based clustering
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. A type of clustering algorithm that groups data points around a central point or centroid, aiming for non-hierarchical structures.
Technical Definition
A type of clustering algorithm that groups data points around a central point or centroid, aiming for non-hierarchical structures.
How it works
Centroid-based clustering is a method for organizing data into clusters where each cluster is defined by a centroid, or center point. Algorithms like k-means fall into this category, assigning data points to the nearest centroid. These methods typically create flat, non-hierarchical groupings of data.
Related Concepts
- Clustering — An unsupervised technique that groups similar data points without using labels.
- Unsupervised Learning — Learning patterns from data that has no labels — only the inputs.
- Data mining — Discovering patterns and insights in large datasets using methods from machine learning, statistics, and databases.