Min-Max-Jump distance and its applications
This work is incremental, as it applies an existing distance metric to enhance specific clustering tasks for data analysis.
The paper tackles the problem of improving clustering and label prediction by applying the Min-Max-Jump distance to revise K-means, Silhouette coefficient, and a neural network model, achieving good performances in these applications.
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.