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 methods based on deep neural networks have proven promising for clustering
real-world data because of their high representational power. In this paper, we propose a …

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

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

Auto-Encoder (AE)-based deep subspace clustering (DSC) methods have achieved
impressive performance due to the powerful representation extracted using deep neural …

We propose DGG: D eep clustering via a G aussian-mixture variational autoencoder (VAE)
with G raph embedding. To facilitate clustering, we apply Gaussian mixture model (GMM) as …

Fuzzy clustering is a classical approach to provide the soft partition of data. Although its
enhancements have been intensively explored, fuzzy clustering still suffers from the …

Deep convolutional neural networks (CNNs) have emerged as a new paradigm for
Mammogram diagnosis. Contemporary CNN-based computer-aided-diagnosis systems …

We propose a self-supervised Gaussian ATtention network for image Clustering
(GATCluster). Rather than extracting intermediate features first and then performing …

Hyperspectral remote sensing organically combines traditional space imaging with
advanced spectral measurement technologies, delivering advantages stemming from …