A survey of clustering with deep learning: From the perspective of network architecture
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 performance highly depends on the quality of data representation. Hence, linear …
A comprehensive survey on deep clustering: Taxonomy, challenges, and future directions
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 …
literature. Classic clustering methods follow the assumption that data are represented as …
Deep clustering with convolutional autoencoders
Deep clustering utilizes deep neural networks to learn feature representation that is suitable
for clustering tasks. Though demonstrating promising performance in various applications …
for clustering tasks. Though demonstrating promising performance in various applications …
Clustering with deep learning: Taxonomy and new methods
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 …
real-world data because of their high representational power. In this paper, we propose a …
Improved deep convolutional embedded clustering with re-selectable sample training
H Lu, C Chen, H Wei, Z Ma, K Jiang, Y Wang - Pattern Recognition, 2022 - Elsevier
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 …
further realize the clustering of samples in the feature space. The existing deep clustering …
Deep clustering: A comprehensive survey
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 …
a good data representation is crucial for clustering algorithms. Recently, deep clustering …
Spectral clustering via ensemble deep autoencoder learning (SC-EDAE)
Several works have studied clustering strategies that combine classical clustering
algorithms and deep learning methods. These strategies generally improve clustering …
algorithms and deep learning methods. These strategies generally improve clustering …
Unsupervised deep embedding for clustering analysis
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 …
extensively in terms of distance functions and grouping algorithms. Relatively little work has …
Unsupervised deep clustering via contractive feature representation and focal loss
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 …
together to learn the clustering-oriented representation, and many approaches have shown …
Deep embedded clustering with data augmentation
Abstract Deep Embedded Clustering (DEC) surpasses traditional clustering algorithms by
jointly performing feature learning and cluster assignment. Although a lot of variants have …
jointly performing feature learning and cluster assignment. Although a lot of variants have …