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 …

Unsupervised representation learning approaches aim to learn discriminative feature
representations from unlabeled data, without the requirement of annotating every sample …

Graph clustering, which aims to divide nodes in the graph into several distinct clusters, is a
fundamental yet challenging task. Benefiting from the powerful representation capability of …

Motivation Single-cell RNA sequencing enables researchers to study cellular heterogeneity
at single-cell level. To this end, identifying cell types of cells with clustering techniques …

We introduce ClusterLLM, a novel text clustering framework that leverages feedback from an
instruction-tuned large language model, such as ChatGPT. Compared with traditional …

Cross-domain recommendation, as a cutting-edge technology to settle data sparsity and
cold start problems, is gaining increasingly popular. Existing research paradigms primarily …

Graph neural networks (GNNs) are now the mainstream method for mining graph-structured
data and learning low-dimensional node-and graph-level embeddings to serve downstream …

In recent years, deep clustering has achieved encouraging results. However, existing deep
clustering methods work with the traditional Euclidean space and thus present deficiency on …

Clustering is one of the most commonly used techniques for unsupervised data analysis. As
real data sets are usually composed of numerical and categorical features that are …

In recommendation systems (RS), user behavior data is observational rather than
experimental, resulting in widespread bias in the data. Consequently, tackling bias has …