# TiK-means: K-means clustering for skewed groups

arXiv: https://arxiv.org/abs/1904.09609 · HTML: https://mlsanity.com/paper/1904.09609/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2019.

## Built on by (most cited first, 2 of 2)

- [Nonparametric clustering for image segmentation](https://mlsanity.com/paper/2101.08345/) (2021)
- [Fast model-based clustering of partial records](https://mlsanity.com/paper/2103.16336/) (2021)

## Abstract (arXiv)

The K-means algorithm is extended to allow for partitioning of skewed groups. Our algorithm is called TiK-Means and contributes a K-means type algorithm that assigns observations to groups while estimating their skewness-transformation parameters. The resulting groups and transformation reveal general-structured clusters that can be explained by inverting the estimated transformation. Further, a modification of the jump statistic chooses the number of groups. Our algorithm is evaluated on simulated and real-life datasets and then applied to a long-standing astronomical dispute regarding the distinct kinds of gamma ray bursts.