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

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

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 …

Text clustering is a task for grou** extracted information of the text in different clusters,
which has many applications in recommender systems, sentiment analysis, and more. Deep …

Deep graph clustering, which aims to group nodes into disjoint clusters by neural networks
in an unsupervised manner, has attracted great attention in recent years. Although the …

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 …

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 …

Precision agriculture technology has become a crucial means of improving the quality of
crop production. As an emerging technology in farmland management, intelligent weeding …

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

Intent learning, which aims to learn users' intents for user understanding and item
recommendation, has become a hot research spot in recent years. However, the existing …