Search Results - Deep autoencoder-based clustering

An Introductory Survey to Autoencoder-based Deep Clustering -- Sandboxes for Combining Clustering with Deep Learning

Authors: Leiber, Collin Miklautz, Lukas Plant, Claudia et al.

Source: CoRR.

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence (cs.AI), 102033 Data Mining, Computer Science - Artificial Intelligence, 102033 Data mining, Machine Learning (cs.LG)

Access URL: http://arxiv.org/abs/2504.02087
https://ucrisportal.univie.ac.at/de/publications/7b1c4e78-4488-4a1c-a224-f6ea35d2b926

Enhancing IoT Intrusion Detection Performance Using Autoencoder-Based Feature Optimization and K-Means Clustering.

Authors: Kebede, Zeru Kifle Hajek, Petr

Source: Procedia Computer Science. 2025, Vol. 270, p3221-3230. 10p.

Subject Terms: Internet of things, Intrusion detection systems (Computer security), Autoencoders, Internet security, Deep learning, Electronic data processing, K-means clustering

Intelligent back-to-back testing with denoising autoencoder-based fault detection and DBSCAN clustering

Authors: Mohammad Abboush Christoph Knieke Andreas Rausch

Source: Results in Engineering, Vol 27, Iss, Pp 105900-(2025)

Subject Terms: Fault detection and clustering, Technology, Back-to-back testing, Deep denoising autoencoder, HIL testing, Real-time validation, Automotive software systems

Access URL: https://doaj.org/article/34c5052e622c4cb18ed2d1499b7ffeea

Matrix Factorization and Deep Autoencoder based Clustering Scheme for Large-scale UAV Networks

Authors: Jiaolan Fang Chan Wang Rongpeng Li et al.

Source: 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring). :1-6

A Novel Autoencoder based Federated Deep Transfer Learning and Weighted k-Subspace Network clustering for Intelligent Intrusion Detection for the Internet of Things

Authors: Lavanya, V. S. Anushiya, R.

Source: Salud, Ciencia y Tecnología - Serie de Conferencias, ISSN 2953-4860, Nº. 3, 2024 (Ejemplar dedicado a: Salud, Ciencia y Tecnología - Serie de Conferencias)

Index Terms: Federated Learning, Transfer Learning, Internet-of-Things, AutoEncoders, Clustering, Feature Selection and Intrusion Detection Systems, text (article)

URL: https://dialnet.unirioja.es/servlet/oaiart?codigo=9872457

Convolutional Autoencoder-based Deep Embedded Fuzzy Clustering Using H-divergences

Source: Academic Journal of Computing & Information Science. 7

Antenna Scanning Type Classification with Autoencoder Based Deep Clustering

Authors: Emirhan Ozmen Yakup Ozkazanc

Source: 2021 29th Signal Processing and Communications Applications Conference (SIU). :1-4

Access URL: https://dblp.uni-trier.de/db/conf/siu/siu2021.html#OzmenO21

A Robust PCA Feature Selection To Assist Deep Clustering Autoencoder-Based Network Anomaly Detection

Authors: Van Quan Nguyen Viet Hung Nguyen Van Loi Cao et al.

Source: 2021 8th NAFOSTED Conference on Information and Computer Science (NICS). :335-341

Access URL: https://researchonline.ljmu.ac.uk/id/eprint/16038/1/1570773925%20stamped.pdf

Operationalization of the construct “Business model of a Bank”: clustering analyses with deep neural networks

Authors: Herdt, Manfred Schulte-Mattler, Hermann

Subject Terms: ddc:330, G21, C45, N24, L21, E58, C38, Business model of banks, Balance sheet characteristics, Cluster analysis, Deep learning, Autoencoders, Deep autoencoder-based clustering

Relation: https://hdl.handle.net/10419/330416

Availability: https://hdl.handle.net/10419/330416
https://doi.org/10.1057/s41261-025-00269-y

DAC–Deep Autoencoder-Based Clustering: A General Deep Learning Framework of Representation Learning

Authors: Ruisi Li Si Lu

Source: Lecture Notes in Networks and Systems ISBN: 9783030821920

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, 03 medical and health sciences, 0302 clinical medicine, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, Machine Learning (cs.LG)

Access URL: http://arxiv.org/pdf/2102.07472
http://arxiv.org/abs/2102.07472

A Novel Autoencoder based Federated Deep Transfer Learning and Weighted k-Subspace Network clustering for Intelligent Intrusion Detection for the Internet of Things

Authors: V. S. Lavanya R. Anushiya

Source: Salud, Ciencia y Tecnología - Serie de Conferencias. 3:648

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

DAC: Deep Autoencoder-based Clustering, a General Deep Learning Framework of Representation Learning

Authors: Lu, Si Li, Ruisi

Index Terms: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, text

URL: http://arxiv.org/abs/2102.07472

Deep Convolutional Asymmetric Autoencoder‐Based Spatial‐Spectral Clustering Network for Hyperspectral Image

Authors: Baisen Liu Weili Kong Yan Wang

Source: Wireless Communications and Mobile Computing. 2022

Subject Terms: 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

File Description: text/xhtml

Cross-Dataset Representation Learning for Unsupervised Deep Clustering in Human Activity Recognition

Authors: Tomoya Takatsu Tessai Hayama Hu Cui

Source: IEEE Access, Vol 13, Pp 74750-74761 (2025)

Subject Terms: representation learning, cross-dataset learning, unsupervised deep clustering, wearable sensors, Human activity recognition, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Access URL: https://doaj.org/article/0b9ae5e3c48c4c548b52de6b233893cd

Clustering Time Series Data through Autoencoder-based Deep Learning Models

Authors: Tavakoli, Neda Siami-Namini, Sima Khanghah, Mahdi Adl et al.

Index Terms: Computer Science - Machine Learning, Statistics - Machine Learning, text

URL: http://arxiv.org/abs/2004.07296

An autoencoder-based deep learning approach for clustering time series data

Authors: Fahimeh Mirza Soltani Mahdi Adl Khanghah Akbar Siami Namin et al.

Source: SN Applied Sciences. 2

Subject Terms: 4. Education, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Access URL: https://link.springer.com/content/pdf/10.1007/s42452-020-2584-8.pdf
https://www.scilit.net/article/4357919462061665630c815ad83b4c7b
https://link.springer.com/content/pdf/10.1007/s42452-020-2584-8.pdf
https://link.springer.com/article/10.1007/s42452-020-2584-8

Day-Ahead Photovoltaic Power Forecasting Using Deep Learning with an Autoencoder-Based Correction Strategy.

Authors: Cortez, Juan Carlos López, Juan Camilo Ullon, Hernan R. et al.

Source: Journal of Control, Automation & Electrical Systems. Aug2024, Vol. 35 Issue 4, p662-676. 15p.

Subject Terms: Deep learning, Electric power distribution grids, Forecasting, K-means clustering, Forecasting methodology, Feature selection

Geographic Terms: Brazil

Big time series data forecasting based on deep autoencoding and clustering.

Authors: Abbasimehr, Hossein Noshad, Ali

Source: Cluster Computing; Aug2025, Vol. 28 Issue 4, p1-26, 26p

Subject Terms: CONVOLUTIONAL neural networks, LONG short-term memory, TIME series analysis, ARTIFICIAL intelligence, TIME-varying networks, DEEP learning

Autoencoder-Based Hyperspectral Unmixing with Simultaneous Number-of-Endmembers Estimation.

Authors: Alshahrani, Atheer Abdullah Bchir, Ouiem Ben Ismail, Mohamed Maher

Source: Sensors (14248220). Apr2025, Vol. 25 Issue 8, p2592. 27p.

Subject Terms: *CLUSTERING algorithms, *AUTOENCODERS, *FUZZY algorithms, *AUTODIDACTICISM, *INDUSTRIAL applications

Unstructured data clustering based on layer improved transformer features connected with deep neural network-based adaptive clustering.

Authors: Jyothi, Bankapalli Sumalatha, L. Eluri, Suneetha

Source: Cluster Computing; Nov2025, Vol. 28 Issue 13, p1-23, 23p

Subject Terms: DEEP learning, TRANSFORMER models, CLUSTER analysis (Statistics), COMPUTER performance, CLUSTERING algorithms, ELECTRONIC data processing, ARTIFICIAL neural networks