# Point-Set Kernel Clustering

arXiv: https://arxiv.org/abs/2002.05815 · HTML: https://mlsanity.com/paper/2002.05815/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2020.

## Built on by (most cited first, 6 of 6)

- [Anomaly Detection Based on Isolation Mechanisms: A Survey](https://mlsanity.com/paper/2403.10802/) (2024)
- [Detecting Change Intervals with Isolation Distributional Kernel](https://mlsanity.com/paper/2212.14630/) (2022)
- [How to Achieve the Intended Aim of Deep Clustering Now, without Deep Learning](https://mlsanity.com/paper/2602.05749/) (2026)
- [Rethinking Divisive Hierarchical Clustering from a Distributional Perspective](https://mlsanity.com/paper/2601.19718/) (2026)
- [Interpretable Graph-Level Anomaly Detection via Contrast with Normal Prototypes](https://mlsanity.com/paper/2602.10708/) (2026)
- [Why does Greedy Search produce Optimal Clustering Outcomes? A Fixed-Core Assignment Theory](https://mlsanity.com/paper/2607.24237/) (2026)

## Abstract (arXiv)

Measuring similarity between two objects is the core operation in existing clustering algorithms in grouping similar objects into clusters. This paper introduces a new similarity measure called point-set kernel which computes the similarity between an object and a set of objects. The proposed clustering procedure utilizes this new measure to characterize every cluster grown from a seed object. We show that the new clustering procedure is both effective and efficient that enables it to deal with large scale datasets. In contrast, existing clustering algorithms are either efficient or effective. In comparison with the state-of-the-art density-peak clustering and scalable kernel k-means clustering, we show that the proposed algorithm is more effective and runs orders of magnitude faster when applying to datasets of millions of data points, on a commonly used computing machine.