# The K-modes algorithm for clustering

arXiv: https://arxiv.org/abs/1304.6478 · HTML: https://mlsanity.com/paper/1304.6478/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2013.

## Built on by (most cited first, 3 of 3)

- [Transductive Few-Shot Learning: Clustering is All You Need?](https://mlsanity.com/paper/2106.09516/) (2021)
- [Simplex Clustering via sBeta with Applications to Online Adjustment of Black-Box Predictions](https://mlsanity.com/paper/2208.00287/) (2022)
- [A Semi-supervised Approach for Activity Recognition from Indoor Trajectory Data](https://mlsanity.com/paper/2301.03134/) (2023)

## Abstract (arXiv)

Many clustering algorithms exist that estimate a cluster centroid, such as K-means, K-medoids or mean-shift, but no algorithm seems to exist that clusters data by returning exactly K meaningful modes. We propose a natural definition of a K-modes objective function by combining the notions of density and cluster assignment. The algorithm becomes K-means and K-medoids in the limit of very large and very small scales. Computationally, it is slightly slower than K-means but much faster than mean-shift or K-medoids. Unlike K-means, it is able to find centroids that are valid patterns, truly representative of a cluster, even with nonconvex clusters, and appears robust to outliers and misspecification of the scale and number of clusters.