Suchergebnisse - "Density-based clustering"

Method for Evaluating Formation Fracturing Effect Utilizing Array Acoustic Logging

Autoren: LI Siyi CHEN Wenhui SUN Xuekai et al.

Quelle: Cejing jishu, Vol 49, Iss 4, Pp 636-642 (2025)

Schlagwörter: unconventional reservoir, shale oil reservoir, sandstone reservoir, acoustic logging, dispersion analysis, density-based clustering, fracturing effect evaluation, Petroleum refining. Petroleum products, TP690-692.5, Technology

Dateibeschreibung: electronic resource

Relation: https://www.cnpcwlt.com/en/#/digest?ArticleID=5772; https://doaj.org/toc/1004-1338

Zugangs-URL: https://doaj.org/article/439d727947fe4caab7328bbce520b6c7

Enhancing patient rehabilitation predictions with a hybrid anomaly detection model: Density‐based clustering and interquartile range methods

Autoren: Murad Ali Khan Jong‐Hyun Jang Naeem Iqbal et al.

Quelle: CAAI Transactions on Intelligence Technology, Vol 10, Iss 4, Pp 983-1006 (2025)

Schlagwörter: anomaly detection, deep learning, density‐based clustering, hybrid model, IQR, regression, Computational linguistics. Natural language processing, P98-98.5, Computer software, QA76.75-76.765

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2468-2322

Zugangs-URL: https://doaj.org/article/bbf0f0bc82564d108d24400d35bf8e21

A scalable multi-density clustering approach to detect city hotspots in a smart city

Autoren: Eugenio Cesario Paolo Lindia Andrea Vinci

Quelle: Future Generation Computer Systems. 157:226-236

Schlagwörter: Smart city, multi density-based clustering, parallel data mining, smart city, Multi density-based clustering, Parallel data mining

Dateibeschreibung: application/pdf

Zugangs-URL: https://www.sciencedirect.com/science/article/pii/S0167739X24001122
https://hdl.handle.net/20.500.14243/467264
https://doi.org/10.1016/j.future.2024.03.042
https://www.sciencedirect.com/science/article/pii/S0167739X24001122
https://doi.org/10.1016/j.future.2024.03.042
https://hdl.handle.net/20.500.11770/364477

Mobility Prediction Based on Deep Learning Approach Using GPS Phone Data

Autoren: null Suhad Faisal Behadil null Nabaa Kareem Mhalhal1

Quelle: Ibn Al-Haitham Journal for Pure and Applied Sciences, Vol 37, Iss 4 (2024)

Schlagwörter: RNNs, Deep Learning, Density-Based Clustering (DBSCAN), Next Location Path, Science, Recurrent Neural Network, Gated Recurrent Unit (GRU)

Zugangs-URL: https://doaj.org/article/24a37b89ec544fe9883848aae740288e

Hierarchical segmentation framework with dynamic parameter optimization for accurate stem-leaf partitioning and phenotypic extraction in maize

Autoren: Yingjie Zhao Dongjie Liu Jiangtan Yao et al.

Quelle: Smart Agricultural Technology, Vol 12, Iss , Pp 101277- (2025)

Schlagwörter: Point cloud segmentation, Maize plant, Phenotypic traits, Density-based clustering, Agriculture (General), S1-972, Agricultural industries, HD9000-9495

Dateibeschreibung: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2772375525005088; https://doaj.org/toc/2772-3755

Zugangs-URL: https://doaj.org/article/787f746059e34b53874210d9ee27d120

Working in Tandem to Uncover 3D Artefact Distribution in Archaeological Excavations: Mathematical Interpretation through Positional and Relational Methods

Autoren: Miguel Ángel Dilena

Quelle: Heritage, Vol 7, Iss 8, Pp 4472-4499 (2024)

Schlagwörter: statistical pattern recognition on archaeological depositions, Archaeology, graphic neural networks (GNN) analysis, 3D density-based clustering techniques, OPTICS method, mathematical interpretation of human occupations, 0601 history and archaeology, 06 humanities and the arts, CC1-960, 3D artefact spatial distribution detection on an archaeological excavation

Zugangs-URL: https://doaj.org/article/6b681b5f94ed4554a294b68a09a59738

Overcoming the cold-start challenge in recommender systems: A novel two-stage framework

Autoren: Matam Vinay Kumar Reddy Nooka Madhusudhana

Quelle: Open Computer Science, Vol 15, Iss 1, Pp pp. 109-132 (2025)

Schlagwörter: cold-start recommendation, hierarchical density-based clustering, transfer learning, bi-gru, matrix factorization, recommendation systems, data sparsity, user/item cold-start, Electronic computers. Computer science, QA75.5-76.95

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2299-1093

Zugangs-URL: https://doaj.org/article/427fc1870dc44ce0a718bdbab9273ffb

The DBCV index is more informative than DCSI, CDbw, and VIASCKDE indices for unsupervised clustering internal assessment of concave-shaped and density-based clusters

Autoren: Davide Chicco Giuseppe Sabino Luca Oneto et al.

Quelle: PeerJ Computer Science, Vol 11, p e3095 (2025)

Schlagwörter: Density-based clustering validation index, Clustering, Unsupervised machine learning, Machine learning, DBSCAN, Internal clustering assessment, Electronic computers. Computer science, QA75.5-76.95

Dateibeschreibung: electronic resource

Relation: https://peerj.com/articles/cs-3095.pdf; https://peerj.com/articles/cs-3095/; https://doaj.org/toc/2376-5992

Zugangs-URL: https://doaj.org/article/dc9b93ccb63f4b7cb7848506991c1a8f

Goal-oriented clustering algorithm to monitor the efficiency of logistic processes through real-time locating systems

Autoren: Pilati F. Sbaragli A. Ruppert T. et al.

Quelle: International Journal of Computer Integrated Manufacturing. 37:1359-1375

Schlagwörter: HE Transportation and Communications / Szállítás, industrial DBscan, internal logistics, Overall equipment effectiveness, density-based clustering, real-time locating systems, hírközlés

Dateibeschreibung: text

Zugangs-URL: https://hdl.handle.net/11572/433674
https://doi.org/10.1080/0951192X.2024.2372272

A Novel Classification Algorithm Based on the Synergy Between Dynamic Clustering with Adaptive Distances and K-Nearest Neighbors

Autoren: Sabri, Mohammed Verde, Rosanna Balzanella, Antonio et al.

Quelle: Journal of Classification. 41:264-288

Schlagwörter: Artificial intelligence, 02 engineering and technology, Pattern recognition (psychology), 7. Clean energy, Clustering Algorithms, Cluster analysis, Artificial Intelligence, Document Clustering, Multi-label Text Classification in Machine Learning, 0202 electrical engineering, electronic engineering, information engineering, FOS: Mathematics, Canopy clustering algorithm, Data mining, Data Clustering Techniques and Algorithms, Spectral Clustering, 4. Education, Correlation clustering, 15. Life on land, k-means clustering, Semi-supervised Clustering, Computer science, Nearest-neighbor chain algorithm, Algorithm, Computer Science, Physical Sciences, Computer Vision and Pattern Recognition, k-nearest neighbors algorithm, Face Recognition and Dimensionality Reduction Techniques, Density-based Clustering, Mathematics

Anomaly detection with the density based spatial clustering of applications with noise (DBSCAN) to detect potentially fraudulent wire transfers

Autoren: Kim, Yongbum Vasarhelyi, Miklos A.

Quelle: Arias Montano. Repositorio Institucional de la Universidad de Huelva
Universidad de Huelva (UHU)

Schlagwörter: 53 Ciencias Económicas, Fraud detection, Anomaly detection, DBSCN, Spatial clustering, Density based clustering, Clustering

Dateibeschreibung: application/pdf

Zugangs-URL: https://hdl.handle.net/10272/23589

Generalization of Single-Linkage with Higher-Order Interactions ; Généralisation de l'algorithme de Single-Linkage aux interactions d'ordre supérieur

Autoren: Hauseux, Louis Avrachenkov, Konstantin Zerubia, Josiane et al.

Weitere Verfasser: Hauseux, Louis Avrachenkov, Konstantin Zerubia, Josiane et al.

Quelle: ISSN: 2364-8228.

Schlagwörter: percolation. 1, Hierarchical clustering, Percolation, simplicial complex, higher-order interaction, geometric graph, density-based clustering, hierarchical clustering density-based clustering geometric graph higher-order interaction simplicial complex percolation. 1, Performance analysis, Hypergraph, Classification hiérarchique, Hypergrahe, Analyse de performance, [INFO]Computer Science [cs], [INFO.INFO-DM]Computer Science [cs]/Discrete Mathematics [cs.DM]

Verfügbarkeit: https://inria.hal.science/hal-05369659
https://inria.hal.science/hal-05369659v1/document
https://inria.hal.science/hal-05369659v1/file/2025_Applied_Network_Science_Generalization_of_Single_Linkage_with_Higher_Order_Interactions_Louis_Hauseux_Konstantin_Avrachenkov_Josiane_Zerubia.pdf

Illuminating the Hierarchical Segmentation of Faults Through an Unsupervised Learning Approach Applied to Clouds of Earthquake Hypocenters

Autoren: Piegari, E. Camanni, G. Mercurio, M. et al.

Quelle: Earth and Space Science, Vol 11, Iss 10, Pp n/a-n/a (2024)

Schlagwörter: density-based clustering, earthquake hypocenter, principal component analysis, segmented fault, unsupervised machine learning, QE1-996.5, Astronomy, density‐based clustering, QB1-991, Geology

Dateibeschreibung: application/pdf

Zugangs-URL: https://doaj.org/article/10a3c10f79144c9ea3a8299e5ebf0c84
https://hdl.handle.net/11588/987712
https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023EA003267

Clustering and Geodesic Scaling of Dissimilarities on the Spherical Surface

Autoren: José Ramón Navarro Vera Ricardo Subiabre Rodrigo Macías

Quelle: Digibug. Repositorio Institucional de la Universidad de Granada
Universidad de Granada (UGR)

Schlagwörter: Geometry, 01 natural sciences, Scaling, Fuzzy Clustering, Cluster analysis, Artificial Intelligence, Document Clustering, Shape Matching and Object Recognition, FOS: Mathematics, 14. Life underwater, 0101 mathematics, 0105 earth and related environmental sciences, Data Clustering Techniques and Algorithms, Geodesic distance, Physics, Statistics, Geometric Morphometrics and Statistical Analysis, 15. Life on land, Semi-supervised Clustering, Computer science, Geodesic, Dissimilarity, 13. Climate action, Physical Sciences, Computer Science, Geometry and Topology, Computer Vision and Pattern Recognition, Surface (topology), Statistical physics, High-Dimensional Data, Mathematics, Density-based Clustering, Multidimensional scaling

Zugangs-URL: https://hdl.handle.net/10481/91493

Applying Density-Based Clustering for the Analysis of Emission Events in Real Driving Emissions Calibration

Autoren: Sascha Krysmon Stefan Pischinger Johannes Claßen et al.

Quelle: Future Transportation, Vol 4, Iss 1, Pp 46-66 (2024)
Future transportation 4(1), 46-66 (2024). doi:10.3390/futuretransp4010004

Schlagwörter: data analysis, 0211 other engineering and technologies, 02 engineering and technology, virtual calibration, Engineering (General). Civil engineering (General), 7. Clean energy, real driving emissions, emission calibration, 13. Climate action, 0202 electrical engineering, electronic engineering, information engineering, TA1-2040, density-based clustering, clustering

Zugangs-URL: https://doaj.org/article/96e34f541c1c4e7d982edd034f334242
https://publications.rwth-aachen.de/record/984718

Vectorized Highly Parallel Density-Based Clustering for Applications With Noise

Autoren: Xavier, Joseph Arnold Pedro Gutiérrez Hermosillo Muriedas, Juan Nassyr, Stepan et al.

Quelle: IEEE Access, Vol 12, Pp 181679-181692 (2024)
IEEE access 12, 181679-181692 (2024). doi:10.1109/ACCESS.2024.3507193
IEEE Access, 12, 181679–181692

Schlagwörter: ddc:004, DATA processing & computer science, vectorization, VHPDBSCAN, Electrical engineering. Electronics. Nuclear engineering, High-performance computing, density-based clustering, TK1-9971, 621.3

Dateibeschreibung: application/pdf

Zugangs-URL: https://doaj.org/article/89f509a67be546e0bbae4250b331d8ca
https://juser.fz-juelich.de/record/1034038
https://publikationen.bibliothek.kit.edu/1000176977
https://publikationen.bibliothek.kit.edu/1000176977/156094104
https://doi.org/10.5445/IR/1000176977

An Improved Density-Based Clustering Algorithm Based on Cellular Neural Networks

Autoren: Jiexi Xu Bingo Wing-Kuen Ling Qing Liu et al.

Quelle: IEEE Access, Vol 12, Pp 138027-138046 (2024)

Schlagwörter: Electrical engineering. Electronics. Nuclear engineering, cellular neural networks, density-based clustering algorithm, unsupervised learning, Clustering, multi-spatial resolutions, TK1-9971

Zugangs-URL: https://doaj.org/article/9e47bdeeaa3f46da83a0f06bce460fdc

FLASC: a flare-sensitive clustering algorithm

Autoren: Daniël M. Bot Jannes Peeters Jori Liesenborgs et al.

Quelle: PeerJ Computer Science, Vol 11, p e2792 (2025)

Schlagwörter: Exploratory data analysis, Density-based clustering, Branch-hierarchy detection, HDBSCAN*, Electronic computers. Computer science, QA75.5-76.95

Dateibeschreibung: electronic resource

Relation: https://peerj.com/articles/cs-2792.pdf; https://peerj.com/articles/cs-2792/; https://doaj.org/toc/2376-5992

Zugangs-URL: https://doaj.org/article/c1cbabccf6bd4bdda4a364428367ffd7

Density-Based Clustering by Means of Bridge Point Identification

Autoren: Colomba, Luca Cagliero, Luca Garza, Paolo

Quelle: IEEE Transactions on Knowledge and Data Engineering. 35:11274-11287

Schlagwörter: clustering, density-based clustering, unsupervised learning, outlier detection, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Dateibeschreibung: application/pdf

How can we theoretically measure the performance of density-based clustering algorithms?

Autoren: Hauseux, Louis Springer

Weitere Verfasser: Hauseux, Louis Springer

Quelle: ACM SIGMETRICS Performance Evaluation Review. 52:19-20

Schlagwörter: [INFO.INFO-DM] Computer Science [cs]/Discrete Mathematics [cs.DM], Nearest Neighbors density estimator, Density estimator, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Percolation, [INFO] Computer Science [cs], Hypergraphs, Geometric graphs, Hierarchical clustering, Density-based clustering

Dateibeschreibung: application/pdf