Industrial products typically lack defects in smart manufacturing systems, which leads to an
extremely imbalanced task of recognizing surface defects. With this imbalanced sample …

Graph data have become increasingly important, and graph node clustering has emerged
as a fundamental task in data analysis. In recent years, graph node clustering has gradually …

Recently deep learning methods have shown significant progress in data clustering tasks.
Deep clustering methods (including distance-based methods and subspace-based …

Abstract Graph Convolutional Network (GCN) has drawn widespread attention in data
mining on graphs due to its outstanding performance and rigor theoretical guarantee …

Deep learning-based clustering methods, especially those incorporating deep generative
models, have recently shown noticeable improvement on many multimedia benchmark …

Multi-view subspace clustering aims to utilize the comprehensive information of multi-source
features to aggregate data into multiple subspaces. Recently, low-rank tensor learning has …

In this paper, we propose an enhanced deep clustering network (EDCN), which is
composed of a Feature Extractor, a Conditional Generator, a Discriminator and a Siamese …

Clustering is a widely used unsupervised learning technique involving an intensive discrete
optimization problem. Associative Memory models or AMs are differentiable neural networks …