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.

翻译：本文探索了Min-Max-Jump距离（MMJ距离）的三种应用。基于MMJ的K均值算法利用MMJ距离改进了K均值聚类；基于MMJ的轮廓系数使用MMJ距离对轮廓系数进行了改进。我们还测试了基于MMJ轮廓系数的神经网络与索引聚类（CNNI）模型。在最后一项应用中，我们在对数据进行聚类分析后，测试了利用Min-Max-Jump距离预测新数据点标签的效果。结果表明，Min-Max-Jump距离在所有三项提出的应用中均取得了良好性能。