1. Consider all data points as one cluster
2. Identify and split the least similar observations
3. Repeat splitting until all clusters are divided

link: [towardsdatascience.com/understanding-the-concept-of-hierarchical-clustering-technique-c6e8243758ec](https://towardsdatascience.com/understanding-the-concept-of-hierarchical-clustering-technique-c6e8243758ec) 

University of Michigan - Ann Arbor

Start with all observations in one cluster, then splits form as one moves down the hierarchy.

Divisive: Top-down approach

Medium: Divisive approach algorithm

- Divisive Clustering takes into account the global distribution of data and so forms more accurate clusters ( i.e. doesn't get thrown off by local minima/maxima ) than Agglomerative Clustering.
- Agglomerative Clustering has O($n^2$) complexity in the most efficient implementation of the algorithm. Divisive Clustering is linear in the number of clusters.

Learn Before

Related