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 …

Ensemble methods are considered the state‐of‐the art solution for many machine learning
challenges. Such methods improve the predictive performance of a single model by training …

Despite significant progress, there remain three limitations to the previous multi-view
clustering algorithms. First, they often suffer from high computational complexity, restricting …

Clustering aims to partition an input dataset into distinct groups according to some distance
or similarity measurements. One of the most widely used clustering method nowadays is the …

Learning graphs from data automatically have shown encouraging performance on
clustering and semisupervised learning tasks. However, real data are often corrupted, which …

Ensemble clustering has been a popular research topic in data mining and machine
learning. Despite its significant progress in recent years, there are still two challenging …

Clustering is an important topic in machine learning and data mining. Recently, deep
clustering, which learns feature representations for clustering tasks using deep neural …

Cluster ensembles have been shown to be better than any standard clustering algorithm at
improving accuracy and robustness across different data collections. This meta-learning …

The clustering ensemble has emerged as an important extension of the classical clustering
problem. It provides an elegant framework to integrate multiple weak base clusterings to …

The advances in single-cell ribonucleic acid sequencing (scRNA-seq) allow researchers to
explore cellular heterogeneity and human diseases at cell resolution. Cell clustering is a …