On the public citation graph

Deep Co-Clustering Algorithm Based on Convolutional Neural Network for Image Data

https://doi.org/10.2139/ssrn.5351939

0 signed · 42 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-70096-0_39openalex-snapshot

Deep Clustering with Convolutional Autoencoders

inferredcites →https://doi.org/10.1007/s10115-015-0823-xopenalex-snapshot

HICC: an entropy splitting-based framework for hierarchical co-clustering

inferredcites →https://doi.org/10.1007/s10994-021-06002-wopenalex-snapshot

A parameter-less algorithm for tensor co-clustering

inferredcites →https://doi.org/10.1007/s11004-021-09935-zopenalex-snapshot

Robust Feature Extraction for Geochemical Anomaly Recognition Using a Stacked Convolutional Denoising Autoencoder

inferredcites →https://doi.org/10.1007/s11042-019-08453-9openalex-snapshot

Dropout vs. batch normalization: an empirical study of their impact to deep learning

inferredcites →https://doi.org/10.1007/s12559-021-09827-8openalex-snapshot

Co-Adjustment Learning for Co-Clustering

inferredcites →https://doi.org/10.1007/s12633-020-00447-9openalex-snapshot

A Cluster-Profile Comparative Study on Machining AlSi7/63% of SiC Hybrid Composite Using Agglomerative Hierarchical Clustering and K-Means

inferredcites →https://doi.org/10.1007/s12665-021-09896-wopenalex-snapshot

Comparison of wave-cluster and DBSCAN algorithms for landslide susceptibility assessment

inferredcites →https://doi.org/10.1016/j.gexplo.2021.106923openalex-snapshot

Application of self-organizing map (SOM) and K-means clustering algorithms for portraying geochemical anomaly patterns in Moalleman district, NE Iran

inferredcites →https://doi.org/10.1016/j.inffus.2021.05.008openalex-snapshot

A review of uncertainty quantification in deep learning: Techniques, applications and challenges

inferredcites →https://doi.org/10.1016/j.knosys.2015.03.027openalex-snapshot

Spectral co-clustering ensemble

inferredcites →https://doi.org/10.1016/j.neucom.2020.12.082openalex-snapshot

Unsupervised deep clustering via adaptive GMM modeling and optimization

inferredcites →https://doi.org/10.1016/j.procs.2020.04.017openalex-snapshot

Clustering Cloud Workloads: K-Means vs Gaussian Mixture Model

inferredcites →https://doi.org/10.1073/pnas.1525793113openalex-snapshot

Co-clustering directed graphs to discover asymmetries and directional communities

inferredcites →https://doi.org/10.1103/physreve.105.044314openalex-snapshot

Degree correlations in graphs with clique clustering

inferredcites →https://doi.org/10.1109/access.2020.2988796openalex-snapshot

Unsupervised K-Means Clustering Algorithm

inferredcites →https://doi.org/10.1109/iccv.2019.00658openalex-snapshot

AttPool: Towards Hierarchical Feature Representation in Graph Convolutional Networks via Attention Mechanism

inferredcites →https://doi.org/10.1109/icdm.2017.141openalex-snapshot

Synchronization-Inspired Co-Clustering and Its Application to Gene Expression Data

inferredcites →https://doi.org/10.1109/icpr48806.2021.9413131openalex-snapshot

N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded Embedding

inferredcites →https://doi.org/10.1109/ijcnn52387.2021.9534368openalex-snapshot

Learning Associations between Features and Clusters: An Interpretable Deep Clustering Method

inferredcites →https://doi.org/10.1109/ijcnn55064.2022.9892322openalex-snapshot

Contrastive Learning and Self-Training for Unsupervised Domain Adaptation in Semantic Segmentation

inferredcites →https://doi.org/10.1109/jiot.2024.3453336openalex-snapshot

Federated Self-Supervised Learning Based on Prototypes Clustering Contrastive Learning for Internet of Vehicles Applications

inferredcites →https://doi.org/10.1109/jiot.2024.3522164openalex-snapshot

Density-Clustering Aggregation for Personalized Federated Learning With AI-Enabled Aerial and Edge Computing in UAVs

inferredcites →https://doi.org/10.1109/lsp.2020.2965328openalex-snapshot

Deep Clustering With Variational Autoencoder

inferredcites →https://doi.org/10.1109/tcyb.2019.2950568openalex-snapshot

Discovering Multiple Co-Clusterings With Matrix Factorization

inferredcites →https://doi.org/10.1109/tetc.2014.2330519openalex-snapshot

A Survey of Clustering Algorithms for Big Data: Taxonomy and Empirical Analysis

inferredcites →https://doi.org/10.1109/tevc.2022.3144134openalex-snapshot

Recombinator-k-Means: An Evolutionary Algorithm That Exploits k-Means++ for Recombination

inferredcites →https://doi.org/10.1109/tii.2019.2896287openalex-snapshot

Bayesian Co-Clustering Truth Discovery for Mobile Crowd Sensing Systems

inferredcites →https://doi.org/10.1109/tnnls.2021.3135460openalex-snapshot

Deep Clustering Analysis via Dual Variational Autoencoder With Spherical Latent Embeddings

inferredcites →https://doi.org/10.1109/tnse.2020.2985096openalex-snapshot

A Differentially Private Big Data Nonparametric Bayesian Clustering Algorithm in Smart Grid

inferredcites →https://doi.org/10.1145/3132847.3133078openalex-snapshot

Non-Exhaustive, Overlapping Co-Clustering

inferredcites →https://doi.org/10.1145/3408318openalex-snapshot

Gaussian Mixture Model Clustering with Incomplete Data

inferredcites →https://doi.org/10.14704/web/v17i2/web17012openalex-snapshot

Development of a Unifying Theory for Data Mining Using Clustering Techniques

inferredcites →https://doi.org/10.1609/aaai.v31i1.10914openalex-snapshot

Scalable Algorithm for Higher-Order Co-Clustering via Random Sampling

inferredcites →https://doi.org/10.18653/v1/2021.naacl-main.427openalex-snapshot

Supporting Clustering with Contrastive Learning

inferredcites →https://doi.org/10.21105/joss.00205openalex-snapshot

hdbscan: Hierarchical density based clustering

inferredcites →https://doi.org/10.24963/ijcai.2017/251openalex-snapshot

Orthogonal and Nonnegative Graph Reconstruction for Large Scale Clustering

inferredcites →https://doi.org/10.3390/rs13204174openalex-snapshot

Self-Organizing Maps for Clustering Hyperspectral Images On-Board a CubeSat

inferredcites →https://doi.org/10.3934/mbe.2022484openalex-snapshot

Achieving deep clustering through the use of variational autoencoders and similarity-based loss

inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-snapshot

Adam: A Method for Stochastic Optimization

inferredcites →https://doi.org/10.48550/arxiv.1907.00481openalex-snapshot

Spectral Clustering with Graph Neural Networks for Graph Pooling

inferredcites →https://doi.org/10.48550/arxiv.2103.12953openalex-snapshot

Supporting Clustering with Contrastive Learning

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.