Min-Max-Jump distance and its applications

01/15/2023

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by   Gangli Liu, et al.

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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.