# Merging K-means with hierarchical clustering for identifying general-shaped groups

arXiv: https://arxiv.org/abs/1712.08786 · HTML: https://mlsanity.com/paper/1712.08786/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2017.

## Built on by (most cited first, 7 of 7)

- [Skeleton Clustering: Dimension-Free Density-based Clustering](https://mlsanity.com/paper/2104.10770/) (2021)
- [Fully Three-dimensional Radial Visualization](https://mlsanity.com/paper/2110.09971/) (2021)
- [UniForCE: The Unimodality Forest Method for Clustering and Estimation of the Number of Clusters](https://mlsanity.com/paper/2312.11323/) (2023)
- [Hierarchical clustering with maximum density paths and mixture models](https://mlsanity.com/paper/2503.15582/) (2025)
- [Nonparametric clustering for image segmentation](https://mlsanity.com/paper/2101.08345/) (2021)
- [Tk-merge: Computationally Efficient Robust Clustering Under General Assumptions](https://mlsanity.com/paper/2201.06391/) (2022)
- [CavMerge: Merging K-means Based on Local Log-Concavity](https://mlsanity.com/paper/2604.04302/) (2026)

## Abstract (arXiv)

Clustering partitions a dataset such that observations placed together in a group are similar but different from those in other groups. Hierarchical and K-means clustering are two approaches but have different strengths and weaknesses. For instance, hierarchical clustering identifies groups in a tree-like structure but suffers from computational complexity in large datasets while K-means clustering is efficient but designed to identify homogeneous spherically-shaped clusters. We present a hybrid non-parametric clustering approach that amalgamates the two methods to identify general-shaped clusters and that can be applied to larger datasets. Specifically, we first partition the dataset into spherical groups using K-means. We next merge these groups using hierarchical methods with a data-driven distance measure as a stopping criterion. Our proposal has the potential to reveal groups with general shapes and structure in a dataset. We demonstrate good performance on several simulated and real datasets.