论文标题
与神经网络和索引聚类
Clustering with Neural Network and Index
论文作者
论文摘要
引入了一种称为神经网络和索引（CNNI）的称为聚类的新模型。 CNNI使用神经网络来聚类数据点。培训神经网络模仿了监督学习，内部聚类评估指数充当损失函数。进行了一个实验以测试新模型的可行性，并与其他聚类模型（如K-均值和高斯混合物模型（GMM））的结果进行了比较。结果表明，CNNI可以在聚类数据中正常工作。配备MMJ-SC的CNNI获得了第一个可以处理非convex形状（非静电几何）数据的参数（电感）聚类模型。
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.
