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.

翻译：本文探索了Min-Max-Jump距离（MMJ距离）的三项应用。基于MMJ的K均值算法使用MMJ距离对K均值进行了修正；基于MMJ的轮廓系数则用MMJ距离改进了轮廓系数。我们还使用基于MMJ的轮廓系数测试了基于神经网络与索引的聚类模型（CNNI）。在最后一项应用中，我们测试了在数据聚类分析后，利用Min-Max-Jump距离预测新数据点的标签。结果表明，Min-Max-Jump距离在所有三项提出的应用中均表现出良好性能。此外，我们设计了若干计算或估计该距离的算法。