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

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 …

Deep clustering utilizes deep neural networks to learn feature representation that is suitable
for clustering tasks. Though demonstrating promising performance in various applications …

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 deep clustering algorithm can learn the latent features of the embedded subspace, and
further realize the clustering of samples in the feature space. The existing deep clustering …

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 …

Several works have studied clustering strategies that combine classical clustering
algorithms and deep learning methods. These strategies generally improve clustering …

Clustering is central to many data-driven application domains and has been studied
extensively in terms of distance functions and grouping algorithms. Relatively little work has …

Deep clustering aims to promote clustering tasks by combining deep learning and clustering
together to learn the clustering-oriented representation, and many approaches have shown …

Abstract Deep Embedded Clustering (DEC) surpasses traditional clustering algorithms by
jointly performing feature learning and cluster assignment. Although a lot of variants have …