  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.
