# Min-Max-Jump distance and its applications

arXiv: https://arxiv.org/abs/2301.05994 · HTML: https://mlsanity.com/paper/2301.05994/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 15 Jan 2023.

## Builds on (most cited first, 5 of 5)

- [Fast and Eager k-Medoids Clustering: O(k) Runtime Improvement of the PAM, CLARA, and CLARANS Algorithms](https://mlsanity.com/paper/2008.05171/) (2020)
- [Path-Based Spectral Clustering: Guarantees, Robustness to Outliers, and Fast Algorithms](https://mlsanity.com/paper/1712.06206/) (2017)
- [A New Index for Clustering Evaluation Based on Density Estimation](https://mlsanity.com/paper/2207.01294/) (2022)
- [Topic Model Supervised by Understanding Map](https://mlsanity.com/paper/2110.06043/) (2021)
- [Clustering with Neural Network and Index](https://mlsanity.com/paper/2212.03853/) (2022)

## Built on by (most cited first, 1 of 1)

- [Internal Evaluation of Density-Based Clusterings with Noise](https://mlsanity.com/paper/2503.00127/) (2025)

## Abstract (arXiv)

We explore three applications of Min-Max-Jump distance (MMJ distance). MMJ-based K-means revises K-means with MMJ distance. MMJ-based Silhouette coefficient revises Silhouette coefficient with MMJ distance. We also tested the Clustering with Neural Network and Index (CNNI) model with MMJ-based Silhouette coefficient. In the last application, we tested using Min-Max-Jump distance for predicting labels of new points, after a clustering analysis of data. Result shows Min-Max-Jump distance achieves good performances in all the three proposed applications. In addition, we devise several algorithms for calculating or estimating the distance.