Search Results - unsupervised clustering-labeling method

Unsupervised selective labeling for semi-supervised industrial defect detection

Authors: Jian Ge Qin Qin Shaojing Song et al.

Source: Journal of King Saud University: Computer and Information Sciences, Vol 36, Iss 8, Pp 102179- (2024)

Subject Terms: Semi-supervised learning, Unsupervised spectral clustering labeling, Industrial defect detection, Electronic computers. Computer science, QA75.5-76.95

File Description: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S1319157824002684; https://doaj.org/toc/1319-1578

Access URL: https://doaj.org/article/0c4a0210eea846ec8f9e8c7b937291ad

The impact of unsupervised feature selection techniques on the performance and interpretation of defect prediction models.

Authors: Li, Zhiqiang Zhu, Wenzhi Zhang, Hongyu et al.

Source: Automated Software Engineering; Nov2025, Vol. 32 Issue 2, p1-48, 48p

TopicFFCA: A short-text topic-detection approach by fuzzy formal concept analysis.

Authors: Mottaghinia, Zeynab Feizi-Derakhshi, Mohammad-Reza Salehpour, Pedram

Source: Cluster Computing; Nov2025, Vol. 28 Issue 14, p1-17, 17p

Subject Terms: SOCIAL media, MACHINE learning, CONTENT analysis, INFORMATION storage & retrieval systems, KEYWORD searching

An Unsupervised Software Fault Prediction Approach Using Threshold Derivation.

Authors: Kumar, Rakesh Chaturvedi, Amrita Kailasam, Lakshmanan

Source: IEEE Transactions on Reliability; Jun2022, Vol. 71 Issue 2, p911-932, 22p

Subject Terms: RANDOM forest algorithms, COMPUTER software quality control, SOFTWARE measurement, SOFTWARE maintenance, COMPUTER software, FORECASTING

A Self-Building and Cluster-Based Cognitive Fault Diagnosis System for Sensor Networks

Authors: ALIPPI, CESARE ROVERI, MANUEL TROVO', FRANCESCO

Source: IEEE Transactions on Neural Networks and Learning Systems. 25:1021-1032

Subject Terms: Big Data, distributed sensors, fault diagnosis, pattern clustering, unsupervised learning, Big Data framework, adaptive learning, cluster-based cognitive fault diagnosis system, clustering algorithm, information content, invariant linear models, nonlinear dynamic systems, parameter space, parameter vectors, self-building-based cognitive fault diagnosis system, sensor networks, unsupervised clustering-labeling method, Adaptation models, Circuit faults, Clustering algorithms, Dictionaries, Training, Vectors, evolving clustering, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 0101 mathematics, 01 natural sciences

File Description: application/pdf

Access URL: https://ieeexplore.ieee.org/document/6781624/
http://dblp.uni-trier.de/db/journals/tnn/tnn25.html#AlippiRT14
https://dblp.uni-trier.de/db/journals/tnn/tnn25.html#AlippiRT14
https://hdl.handle.net/11311/866935
https://doi.org/10.1109/TNNLS.2014.2303651

An unsupervised software bug count prediction model based on selected software metrics.

Authors: Kumar, Rakesh Chaturvedi, Amrita

Source: Applied Intelligence; Jul2025, Vol. 55 Issue 10, p1-27, 27p

Unsupervised selective labeling for semi-supervised industrial defect detection.

Authors: Ge, Jian Qin, Qin Song, Shaojing et al.

Source: Journal of King Saud University - Computer & Information Sciences. Oct2024, Vol. 36 Issue 8, pN.PAG-N.PAG. 1p.

Subject Terms: Metallic surfaces, Printed circuits, Surface defects, Annotations, Integrals

Optimizing Tuition Fee Determination with K-Means Cluster Relabeling Based on Centroid Mapping of Principal Component Pattern.

Authors: Yustanti, Wiyli Nurhidayat, Andi Iwan Java, Muhammad Iskandar

Source: Journal of Information Systems Engineering & Business Intelligence; Oct2025, Vol. 11 Issue 3, p445-458, 14p

From Clusters to Communities: Enhancing Wetland Vegetation Mapping Using Unsupervised and Supervised Synergy.

Authors: Wen, Li Ryan, Shawn Powell, Megan et al.

Source: Remote Sensing; Jul2025, Vol. 17 Issue 13, p2279, 22p

Subject Terms: VEGETATION mapping, WETLANDS, SAMPLING (Process), REMOTE sensing, HYDROLOGY, RANDOM forest algorithms, NATURE conservation

Geographic Terms: AUSTRALIA

A Hybrid Deep Learning-Based Architecture for Network Traffic Anomaly Detection via EFMS-Enhanced KMeans Clustering and CNN-GRU Models.

Authors: Quirumbay Yagual, Daniel Fernández Iglesias, Diego Nóvoa, Francisco J.

Source: Applied Sciences (2076-3417); Oct2025, Vol. 15 Issue 20, p10889, 29p

Subject Terms: ANOMALY detection (Computer security), DEEP learning, INTERNET security, DATA security failures, CLUSTERING algorithms, COMPUTER network traffic

Clustering Algorithms and RAG Enhancing Semi-Supervised Text Classification with Large LLMs.

Authors: Zhong, Shan Zeng, Jiahao Yu, Yongxin et al.

Source: International Journal of Data Science & Analytics; Nov2025, Vol. 20 Issue 6, p5377-5398, 22p

Efficient UAV-Based Automatic Classification of Cassava Fields Using K-Means and Spectral Trend Analysis.

Authors: Boonrang, Apinya Piyatadsananon, Pantip Sritarapipat, Tanakorn

Source: AgriEngineering; Dec2024, Vol. 6 Issue 4, p4406-4424, 19p

Subject Terms: AGRICULTURAL drones, IMAGE recognition (Computer vision), AUTOMATIC classification, SOIL classification, DRONE aircraft

ConCPDP: A Cross‐Project Defect Prediction Method Integrating Contrastive Pretraining and Category Boundary Adjustment.

Authors: Song, Hengjie Pan, Yufei Guo, Feng et al.

Source: IET Software (Wiley-Blackwell); 11/13/2024, Vol. 2024, p1-19, 19p

Subject Terms: DATA augmentation, FEATURE extraction, COMPUTER software testing, STATISTICAL correlation, PREDICTION models, DEEP learning

Unsupervised Bug Report Categorization Using Clustering and Labeling Algorithm.

Authors: Limsettho, Nachai Hata, Hideaki Monden, Akito et al.

Source: International Journal of Software Engineering & Knowledge Engineering; Sep2016, Vol. 26 Issue 7, p1027-1053, 27p

Subject Terms: SUPERVISED learning, ALGORITHMS, SOFTWARE engineering, DEBUGGING, NATURAL language processing

Empirical and Experimental Insights into Data Mining Techniques for Crime Prediction: A Comprehensive Survey.

Authors: Taha, Kamal

Source: ACM Transactions on Intelligent Systems & Technology; Apr2025, Vol. 16 Issue 2, p1-75, 75p

Subject Terms: MACHINE learning, PREDICTION algorithms, DEEP learning, DATA mining, CRIME analysis, STATISTICAL learning

A numerical evaluation of real-time workloads for ramp controller through optimization of multi-type feature combinations derived from eye tracker, respiratory, and fatigue patterns.

Authors: Shao, Quan Jiang, Kaiyue Li, Ruoheng

Source: PLoS ONE; 11/8/2024, Vol. 19 Issue 11, p1-30, 30p

Subject Terms: WEIGHT training, AERONAUTICAL safety measures, DATA extraction, ACQUISITION of data, CLASSIFICATION

ENHANCING PATTERN SEQUENCE-BASED FORECASTS: A MODIFIED STRATEGY RELATIVE TO ELECTRICAL LOAD.

Authors: Annamareddi, Suseelatha Gopinathan, Sudheer

Source: Reliability: Theory & Applications; 2025, Vol. 20, p301-311, 11p

Subject Terms: CLUSTERING algorithms, ELECTRICAL load, ELECTRIC power consumption, TIME series analysis, FORECASTING, HIERARCHICAL clustering (Cluster analysis), LOAD forecasting (Electric power systems)

Unsupervised learning and natural language processing highlight research trends in a superbug.

Authors: Méndez-Cruz, Carlos-Francisco Rodríguez-Herrera, Joel Varela-Vega, Alfredo et al.

Source: Frontiers in Artificial Intelligence; 2024, p1-11, 11p

An overview of the history of Science of Science in China based on the use of bibliographic and citation data: a new method of analysis based on clustering with feature maximization and contrast graphs.

Authors: Lamirel, Jean-Charles Chen, Yue Cuxac, Pascal et al.

Source: Scientometrics; 2020, Vol. 125 Issue 3, p2971-2999, 29p

Artificial neural networks for data mining in animal sciences.

Authors: Hamadani, Ambreen Ganai, Nazir Ahmad Bashir, Janibul

Source: Bulletin of the National Research Centre; 5/16/2023, Vol. 47 Issue 1, p1-8, 8p

Subject Terms: ARTIFICIAL neural networks, DATA mining, ANIMAL science, SUPERVISED learning, PATTERN recognition systems, DATABASE management