Cluster analysis plays an indispensable role in machine learning and data mining. Learning
a good data representation is crucial for clustering algorithms. Recently, deep clustering …

Clustering is a fundamental machine learning task, which aim at assigning instances into
groups so that similar samples belong to the same cluster while dissimilar samples belong …

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

Clustering is a fundamental task in data analysis. Recently, deep clustering, which derives
inspiration primarily from deep learning approaches, achieves state-of-the-art performance …

Graph-based subspace clustering methods have exhibited promising performance.
However, they still suffer some of these drawbacks: they encounter the expensive time …

Subspace clustering is an effective method that has been successfully applied to many
applications. Here, we propose a novel subspace clustering model for multi-view data using …

Clustering is a fundamental problem in many data-driven application domains, and
clustering performance highly depends on the quality of data representation. Hence, linear …

The similarity among samples and the discrepancy among clusters are two crucial aspects
of image clustering. However, current deep clustering methods suffer from inaccurate …

By contrasting positive-negative counterparts, graph contrastive learning has become a
prominent technique for unsupervised graph representation learning. However, existing …

Abstract Models trained for classification often assume that all testing classes are known
while training. As a result, when presented with an unknown class during testing, such …