A new model called Clustering with Neural Network and Index (CNNI) is introduced. CNNI uses a Neural Network to cluster data points. Training of the Neural Network mimics supervised learning, with an internal clustering evaluation index acting as the loss function. An experiment is conducted to test the feasibility of the new model, and compared with results of other clustering models like K-means and Gaussian Mixture Model (GMM). The result shows CNNI can work properly for clustering data; CNNI equipped with MMJ-SC, achieves the first parametric (inductive) clustering model that can deal with non-convex shaped (non-flat geometry) data.

翻译：提出一种名为基于神经网络与指标的聚类（CNNI）的新模型。CNNI利用神经网络对数据点进行聚类，其训练过程模仿监督学习范式，以内部聚类评估指标作为损失函数。通过实验验证新模型的可行性，并将其结果与K均值和高斯混合模型（GMM）等其他聚类模型进行对比。结果表明，CNNI能够有效执行数据聚类；配备MMJ-SC指标的CNNI成为首个能够处理非凸形状（非平坦几何）数据的参数化（归纳式）聚类模型。