论文标题
NN-EVCLUS：基于神经网络的证据聚类
NN-EVCLUS: Neural Network-based Evidential Clustering
论文作者
论文摘要
证据聚类是一种基于Dempster-Shafer质量函数来表示群集成员不确定性的方法。在本文中，我们介绍了一种基于神经网络的证据聚类算法，称为NN-EVCLUS，该算法从属性向量到质量函数学习了映射，以使更相似的输入映射到较低冲突程度的输出质量函数。神经网络可以与一级支撑向量机配对，以使其与异常值的稳定性并允许新颖性检测。对网络进行了训练，以最大程度地减少所有或某些对象对的差异和冲突程度之间的差异。可以将其他术语添加到损失函数中以说明成对约束或标记的数据，这也可以用于调整度量标准。比较实验表明，对于一系列涉及属性和相似性数据的无监督和约束聚类任务，N-evclus比最先进的证据聚类算法优越。
Evidential clustering is an approach to clustering based on the use of Dempster-Shafer mass functions to represent cluster-membership uncertainty. In this paper, we introduce a neural-network based evidential clustering algorithm, called NN-EVCLUS, which learns a mapping from attribute vectors to mass functions, in such a way that more similar inputs are mapped to output mass functions with a lower degree of conflict. The neural network can be paired with a one-class support vector machine to make it robust to outliers and allow for novelty detection. The network is trained to minimize the discrepancy between dissimilarities and degrees of conflict for all or some object pairs. Additional terms can be added to the loss function to account for pairwise constraints or labeled data, which can also be used to adapt the metric. Comparative experiments show the superiority of N-EVCLUS over state-of-the-art evidential clustering algorithms for a range of unsupervised and constrained clustering tasks involving both attribute and dissimilarity data.
