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Min-Max-Jump distance and its applications

2023-01-15 00:55:40
Gangli Liu

Abstract

A new distance metric called Min-Max-Jump distance (MMJ distance) is proposed. Three applications of it are tested. 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.

Abstract (translated)

URL

https://arxiv.org/abs/2301.05994

PDF

https://arxiv.org/pdf/2301.05994.pdf


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