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 has been widely studied in the
literature. Classic clustering methods follow the assumption that data are represented as …

We present a new framework for semantic segmentation without annotations via clustering.
Off-the-shelf clustering methods are limited to curated, single-label, and object-centric …

Unsupervised domain adaptation (UDA) is to make predictions for unlabeled data on a
target domain, given labeled data on a source domain whose distribution shifts from the …

Deep Learning (DL) has shown great promise in the unsupervised task of clustering. That
said, while in classical (ie, non-deep) clustering the benefits of the nonparametric approach …

Deep clustering is a fundamental yet challenging task for data analysis. Recently we witness
a strong tendency of combining autoencoder and graph neural networks to exploit structure …

With the development of web technology, multi-modal or multi-view data has surged as a
major stream for big data, where each modal/view encodes individual property of data …

Multi-view clustering, a long-standing and important research problem, focuses on mining
complementary information from diverse views. However, existing works often fuse multiple …

Single-cell RNA sequencing (scRNA-seq) is a revolutionary technology to determine the
precise gene expression of individual cells and identify cell heterogeneity and …

Clustering large and high-dimensional document data has got a great interest. However,
current clustering algorithms lack efficient representation learning. Implementing deep …