Intermediate · Data
Divisive clustering
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. A hierarchical clustering method that starts with all data points in one cluster and progressively splits them.
Technical Definition
A hierarchical clustering method that starts with all data points in one cluster and progressively splits them.
How it works
Divisive clustering is a top-down approach to hierarchical clustering. It begins by assigning all data points to a single cluster. Then, it iteratively splits the most heterogeneous cluster into smaller sub-clusters until a stopping criterion is met, such as reaching a desired number of clusters or a minimum cluster size.
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.
- Agglomerative clustering — Agglomerative clustering is a bottom-up hierarchical clustering method that iteratively merges the closest clusters.
- Hierarchical clustering — An algorithm that creates a tree-like structure of nested clusters.