Suchergebnisse - Clustering- algorithms. General Terms Algorithms

Comparative benchmarking of single-cell clustering algorithms for transcriptomic and proteomic data

Autoren: Yu-Hang Yin Fang Wang Wei Li et al.

Quelle: Genome Biology, Vol 26, Iss 1, Pp 1-34 (2025)

Schlagwörter: Single-cell clustering, Transcriptomics, Proteomics, Feature integration, Biology (General), QH301-705.5, Genetics, QH426-470

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/1474-760X

Zugangs-URL: https://doaj.org/article/a230f24b88744705bf11f4292c6ca45d

PMWFCM: A Possibility based MultiKernel Weighted Fuzzy Clustering Algorithm for classification of driving behaviors

Autoren: Ning Ma Kaijun Wu Yubin Yuan et al.

Quelle: Alexandria Engineering Journal, Vol 113, Iss , Pp 249-261 (2025)

Schlagwörter: Fuzzy clustering algorithm, Possibilistic fuzzy clustering, Kernel function, Traffic driving assessment, Engineering (General). Civil engineering (General), TA1-2040

Dateibeschreibung: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S1110016824014959; https://doaj.org/toc/1110-0168

Zugangs-URL: https://doaj.org/article/43cb12dcf3a14505be449c45f340a853

An Algorithm for Clustering with Confidence-Based Must-Link and Cannot-Link Constraints.

Autoren: Baumann, Philipp Hochbaum, Dorit S.

Quelle: INFORMS Journal on Computing. Jul/Aug2025, Vol. 37 Issue 4, p1044-1068. 25p.

Schlagwörter: *INTEGER programming, CLUSTERING algorithms, SUPERVISED learning

Clustering of listed stock exchange companies active in the cement using the FPC clustering algorithm

Autoren: Leila Safari-monjeghtapeh Mansour Esmaeilpour

Quelle: Data-Centric Engineering, Vol 5 (2024)

Schlagwörter: clustering, FDPC algorithm, FPC algorithm, GOPC algorithm, IPC algorithm, K-means algorithm, stock market prediction, Engineering (General). Civil engineering (General), TA1-2040

Dateibeschreibung: electronic resource

Relation: https://www.cambridge.org/core/product/identifier/S2632673624000364/type/journal_article; https://doaj.org/toc/2632-6736

Zugangs-URL: https://doaj.org/article/0168b70863a84e4bb055e6b33ebec991

Improved Fingerprint-Based Localization Based on Sequential Hybridization of Clustering Algorithms

Autoren: Abdulmalik Shehu Yaro Filip Maly Pavel Prazak et al.

Quelle: Emerging Science Journal, Vol 8, Iss 2, Pp 394-406 (2024)

Schlagwörter: clustering, closest ap, k-nn, sequential hybrid, localization accuracy, rss, k-means., Technology (General), T1-995, Social sciences (General), H1-99

Dateibeschreibung: electronic resource

Relation: https://www.ijournalse.org/index.php/ESJ/article/view/2192; https://doaj.org/toc/2610-9182

Zugangs-URL: https://doaj.org/article/cf1a5a3d04c64ffc916c1d93ef50689a

DynamicCluStream: An algorithm Based on CluStream to Improve Clustering Quality

Autoren: Sahar Ahsani Morteza Yousef Sanati Muharram Mansoorizadeh

Quelle: International Journal of Web Research, Vol 6, Iss 2, Pp 77-87 (2023)

Schlagwörter: data mining, online clustering, dynamic clustering, stream clustering, clustream, dynamic clustream, Information resources (General), ZA3040-5185

Dateibeschreibung: electronic resource

Relation: https://ijwr.usc.ac.ir/article_195498_ecf67997072fbc813f3c0b5311fd59f2.pdf; https://doaj.org/toc/2645-4343

Zugangs-URL: https://doaj.org/article/359a97501c8d456589339eaa0cf63a4e

Classic action recognition of cross-country skiing sports combining ant colony and clustering algorithm.

Autoren: Yu, Hongbo Li, Yuanhui

Quelle: BMC Sports Science, Medicine & Rehabilitation; 11/13/2025, Vol. 17 Issue 1, p1-15, 15p

Schlagwörter: CROSS-country skiing, ANT algorithms, CLUSTERING algorithms, HUMAN activity recognition, SPORTS & technology, ATHLETIC ability, K-means clustering

Could ChatGPT Automate Water Network Clustering? A Performance Assessment Across Algorithms.

Autoren: Palma, Ludovica Creaco, Enrico Iervolino, Michele et al.

Quelle: Water (20734441); Oct2025, Vol. 17 Issue 20, p2995, 13p

Schlagwörter: CHATGPT, CLUSTERING algorithms, ARTIFICIAL intelligence, AUTOMATION, HYDRAULIC models, OPERATIONS research, ENVIRONMENTAL infrastructure, DECISION making

Towards lowering computational power in IoT systems: Clustering algorithm for high-dimensional data stream using entropy window reduction

Autoren: Gamal Alkawsi Redhwan Al-amri Yahia Baashar et al.

Quelle: Alexandria Engineering Journal, Vol 70, Iss , Pp 503-513 (2023)

Schlagwörter: Anomaly detection, Clustering, Computational power, Data stream, Entropy, High-dimensionality, Engineering (General). Civil engineering (General), TA1-2040

Dateibeschreibung: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S1110016823001692; https://doaj.org/toc/1110-0168

Zugangs-URL: https://doaj.org/article/67059100849740f288bb58d06d176f34

Application of nonlinear clustering optimization algorithm in web data mining of cloud computing

Autoren: Zhang Yan

Quelle: Nonlinear Engineering, Vol 12, Iss 1, Pp 1-8 (2023)

Schlagwörter: nonlinear clustering optimization, cloud computing, data mining, pbm-index, db-index, Engineering (General). Civil engineering (General), TA1-2040

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2192-8029

Zugangs-URL: https://doaj.org/article/95a2618613e1462095cdaf98ee392f23

Detecting IoT Anomalies Using Fuzzy Subspace Clustering Algorithms

Autoren: Mohamed Shenify Fokrul Alom Mazarbhuiya A. S. Wungreiphi

Quelle: Applied Sciences, Vol 14, Iss 3, p 1264 (2024)

Schlagwörter: anomaly detection, information system, high-dimensional data, dominance relation, fuzzy clustering method, CORE of attribute set, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2076-3417/14/3/1264; https://doaj.org/toc/2076-3417

Zugangs-URL: https://doaj.org/article/1587bf27923f4f97bab06e6b30798d0c

Genetic based density peak possibilistic fuzzy c-means algorithms to cluster analysis- a case study on customer segmentation

Autoren: R.J. Kuo Muhammad Naufal Alfareza Thi Phuong Quyen Nguyen

Quelle: Engineering Science and Technology, an International Journal, Vol 47, Iss , Pp 101525- (2023)

Schlagwörter: Customer segmentation, Density peak clustering, Genetic algorithm, Possibilistic fuzzy c-means algorithm, Engineering (General). Civil engineering (General), TA1-2040

Dateibeschreibung: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2215098623002033; https://doaj.org/toc/2215-0986

Zugangs-URL: https://doaj.org/article/cb4d291459cf44978100842139d982f6

Matrix Factorization-Based Clustering for Sparse Data in Recommender Systems: A Comparative Study.

Autoren: Bojorque, Rodolfo Hurtado, Remigio

Quelle: Computation; Sep2025, Vol. 13 Issue 9, p213, 14p

Schlagwörter: CLUSTERING algorithms, RECOMMENDER systems, METADATA, MATRIX decomposition, CONSUMER preferences

Treatment journey clustering with a novel kernel k-means machine learning algorithm: a retrospective analysis of insurance claims in bipolar I disorder.

Autoren: Littman, Matthew Nguyen, Huy-Binh Campbell, Joanna et al.

Quelle: Brain Informatics; 5/22/2025, Vol. 12 Issue 1, p1-18, 18p

Schlagwörter: BIPOLAR disorder, MACHINE learning, CLUSTERING algorithms, K-means clustering, INSURANCE claims, MEDICAL care use, THERAPEUTICS

Beef Breeding Systems and Preferences for Breeding Objective Traits.

Autoren: Krupová, Zuzana Krupa, Emil Brzáková, Michaela et al.

Quelle: Animals (2076-2615); Aug2025, Vol. 15 Issue 15, p2175, 20p

Schlagwörter: ANIMAL breeding, CATTLE breeding, BEEF industry, CLUSTERING algorithms, CATTLE reproduction, PHENOTYPES

Geografische Kategorien: CZECH Republic

Scalable Clustering of Complex ECG Health Data: Big Data Clustering Analysis with UMAP and HDBSCAN.

Autoren: Kaverinskiy, Vladislav Chaikovsky, Illya Mnevets, Anton et al.

Quelle: Computation; Jun2025, Vol. 13 Issue 6, p144, 21p

Schlagwörter: CLUSTERING algorithms, HEART failure patients, HEART beat, HEART failure, EUCLIDEAN distance

Unsupervised clustering for sepsis identification in large-scale patient data: a model development and validation study.

Autoren: Li, Na Riazi, Kiarash Pan, Jie et al.

Quelle: Intensive Care Medicine Experimental; 7/7/2025, Vol. 13 Issue 1, p1-12, 12p

Schlagwörter: SEPSIS, CLUSTERING algorithms, MODEL validation, MEDICAL records, MACHINE learning, EPIDEMIOLOGY, MEDICAL coding, NOSOCOMIAL infections

Clustering of behavioral and chronic health risk factors and their association with self-reported health and cardiovascular disease outcome among adults in North Carolina.

Autoren: Ogbu, Chukwuemeka E Ogbu, Stella C Ezechukwu, Maureen et al.

Quelle: AIMS Public Health; 2025, Vol. 12 Issue 3, p1-25, 25p

Schlagwörter: CARDIOVASCULAR diseases, DISEASE risk factors, CLUSTERING algorithms, CHRONIC diseases, HEALTH promotion, HEALTH status indicators, HEALTH behavior

Geografische Kategorien: NORTH Carolina

Comparison of machine learning clustering algorithms for detecting heterogeneity of treatment effect in acute respiratory distress syndrome: A secondary analysis of three randomised controlled trials

Autoren: Pratik Sinha Alexandra Spicer Kevin L Delucchi et al.

Quelle: EBioMedicine, Vol 74, Iss , Pp 103697- (2021)

Schlagwörter: ARDS, RCTs, Clustering, machine learning, LCA, Heterogeneity of treatment effect, Medicine, Medicine (General), R5-920

Dateibeschreibung: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2352396421004916; https://doaj.org/toc/2352-3964

Zugangs-URL: https://doaj.org/article/dcd0c2641c8d430e8bbc69ed3dea44b9

K-Means-Based Nature-Inspired Metaheuristic Algorithms for Automatic Data Clustering Problems: Recent Advances and Future Directions

Autoren: Abiodun M. Ikotun Mubarak S. Almutari Absalom E. Ezugwu

Quelle: Applied Sciences, Vol 11, Iss 23, p 11246 (2021)

Schlagwörter: K-means clustering, automatic clustering, nature-inspired metaheuristic algorithms, cluster analysis, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2076-3417/11/23/11246; https://doaj.org/toc/2076-3417

Zugangs-URL: https://doaj.org/article/96831dd5adf544b5a783982dd1f5bb67