mlsanityData to 7 Oct 2026

Min-Max-Jump distance and its applications

arXiv 2301.05994 ↗First posted 15 Jan 2023cs.CVcited by 1

Not on the map. Why?

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 5 of its 20 references that are in the map. First posted on arXiv 15 Jan 2023.

Builds on

5 of 20 references.

References · 5

  1. 2020Fast and Eager k-Medoids Clustering: O(k) Runtime Improvement of the PAM, CLARA, and CLARANS Algorithmscited by 35
  2. 2017Path-Based Spectral Clustering: Guarantees, Robustness to Outliers, and Fast Algorithmscited by 17
  3. 2022A New Index for Clustering Evaluation Based on Density Estimationcited by 3
  4. 2021Topic Model Supervised by Understanding Mapcited by 2
  5. 2022Clustering with Neural Network and Indexcited by 1

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Internal Evaluation of Density-Based Clusterings with Noisecited by 1