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：我们探索了 Min-Max-Jump 距离（MMJ距离）的三个应用。基于 MMJ 距离的K-means 算法用 MMJ 距离来修正 K-means; 基于 MMJ 距离的 Silhouette 系数用于修正 Silhouette 系数，同时还测试了基于 MMJ 距离的 Silhouette 系数的集群神经网络和指数（CNNI）模型。在最后一个应用中，我们测试了将 Min-Max-Jump 距离用于预测数据聚类分析后的新数据点的标签。结果表明，Min-Max-Jump 距离在所有三个提出的应用程序中都表现出良好的性能。
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