Iterative clustering is a method for clustering points with the goal of making evenly-sized clusters. It can be considered a really basic way to perform hierarchical clustering.

Some clustering algorithms may yield sub-optimal results when asked for a large number of clusters. While this is generally not a problem for unsupervised ML, it is a problem if you know how many clusters you are going to need, for example when splitting similar data into N clusters.

Let’s say you want to end up with *K* clusters. Instead of asking the clustering algorithm for K clusters, just ask it to split your data in two.

- Find the biggest cluster, or the only cluster
- Split the points into two clusters
- If there are K distinct cluster labels, stop. Otherwise, go to 1.

### Citation

If you find this work useful, please cite it as:

@article{yaltirakliwikiiterativeclustering,
title = "Iterative clustering",
author = "Yaltirakli, Gokberk",
journal = "gkbrk.com",
year = "2023",
url = "https://www.gkbrk.com/wiki/iterative-clustering/"
}

## Not using BibTeX? Click here for more citation styles.

**IEEE Citation** Gokberk Yaltirakli, "Iterative clustering", December, 2023. [Online]. Available: https://www.gkbrk.com/wiki/iterative-clustering/. [Accessed Dec. 04, 2023].

**APA Style** Yaltirakli, G. (2023, December 04). Iterative clustering. https://www.gkbrk.com/wiki/iterative-clustering/

**Bluebook Style** Gokberk Yaltirakli, *Iterative clustering*, GKBRK.COM (Dec. 04, 2023), https://www.gkbrk.com/wiki/iterative-clustering/