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检索条件"主题词=clustering algorithms"
51277 条 记 录,以下是441-450 订阅
排序:
Unsupervised Competitive Learning clustering and Visual Method to Obtain Accurate Trajectories From Noisy Repetitive GPS Data
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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年 第2期26卷 1562-1572页
作者: Mariotto, Flavio Tonioli Yoma, Nestor Becerra de Almeida, Madson Cortes Univ Estadual Campinas Fac Engn Elect & Computacao FEEC Dept Sistema Energia DSE BR-13083970 Campinas Brazil Univ Chile Dept Elect Engn Santiago 8370451 Chile
To make the proper planning of bus public transportation systems, especially with the introduction of electric buses to the fleets, it is essential to characterize the routes, patterns of traffic, speed, constraints, ... 详细信息
来源: 评论
TRUNC: A Transfer Learning Unsupervised Network for Data clustering
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IEEE ACCESS 2025年 13卷 46282-46298页
作者: Xavier, Rita Peller, John de Castro, Leandro Nunes Univ Estadual Campinas Sch Technol BR-13484332 Limeira SP Brazil Florida Gulf Coast Univ Dendritic Human Ctr AI & Data Sci Inst Dept Comp & Software Engn Ft Myers FL 10501 USA
There is a demand for effective clustering methods, especially given the increasing complexity of data scenarios in modern applications. Motivated by the limitations of traditional clustering algorithms, particularly ... 详细信息
来源: 评论
Scalable Semi-Supervised clustering via Structural Entropy With Different Constraints
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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2025年 第1期37卷 478-492页
作者: Zeng, Guangjie Peng, Hao Li, Angsheng Wu, Jia Liu, Chunyang Yu, Philip S. Beihang Univ State Key Lab Software Dev Environm Beijing 1000191 Peoples R China Macquarie Univ Dept Comp Sydney NSW 2109 Australia Didi Chuxing Beijing 100193 Peoples R China Univ Illinois Dept Comp Sci Chicago IL 60607 USA
Semi-supervised clustering leverages prior information in the form of constraints to achieve higher-quality clustering outcomes. However, most existing methods struggle with large-scale datasets owing to their high ti... 详细信息
来源: 评论
Unsupervised Software Defect Prediction Through Multiview clustering
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IEEE TRANSACTIONS ON RELIABILITY 2025年
作者: Li, Zhiqiang Zhang, Hongyu Jing, Xiao-Yuan Yu, Wangyang Liu, Yueyue Shaanxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China Chongqing Univ Sch Big Data & Software Engn Chongqing 401331 Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China Guangdong Univ Petrochem Technol Guangdong Prov Key Lab Petrochem Equipment Fault D Maoming 525011 Peoples R China Univ Newcastle Sch Informat & Phys Sci Callaghan NSW 2308 Australia
The core goal of software defect prediction (SDP) is to identify modules with a high likelihood of defects, thereby enabling prioritization of quality assurance activities with low inspection effort. There are many su... 详细信息
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LT-PEM Fuel Cells Diagnosis Based on EIS, clustering, and Automatic Parameter Selection
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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年 第2期74卷 2513-2526页
作者: Chanal, Damien Steiner, Nadia Yousfi Chamagne, Didier Pera, Marie-Cecile Univ Franche Comte FEMTO ST Inst CNRS FCLAB F-90000 Belfort France
In the field of fuel cells, early detection of faulty conditions can significantly improve the lifetime. Then, signal analysis techniques such as electrochemical impedance spectroscopy combined with machine learning a... 详细信息
来源: 评论
Deep Spectral clustering With Projected Adaptive Feature Selection
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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷
作者: Zhao, Yang Bi, Zixuan Zhu, Peican Yuan, Aihong Li, Xuelong Northwestern Polytech Univ OPt & Elect iOPEN Sch Artificial Intelligence Xian 710072 Peoples R China Shanghai Artificial Intelligence Lab Shanghai 200232 Peoples R China Northwest A&F Univ Coll Informat Engn Yangling 712100 Peoples R China China Telecom Corp Ltd Inst Artificial Intelligence Tele AI Beijing 100033 Peoples R China
In the past era of explosive data growth, how to deal with large-scale, unlabeled remote sensing images (RSIs) has become a concern. Due to the lack of data labels, unsupervised methods are usually used to deal with t... 详细信息
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A Topological-Indicators-Based k-Means clustering Algorithm and Its Application in Time Series Data: A Case Study on Sea Level Variability in Peninsular Malaysia
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IEEE ACCESS 2025年 13卷 46514-46533页
作者: Lin, Zixin Zulkepli, Nur Fariha Syaqina Kasihmuddin, Mohd Shareduwan Bin Mohd Gobithaasan, Rudrusamyr Univ Sains Malaysia USM Sch Math Sci George Town 11800 Penang Malaysia
Traditional k-means clustering is widely used to analyze regional and temporal variations in time series data, such as sea levels. However, its accuracy can be affected by limitations, particularly when applied to dat... 详细信息
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Sparse Multi-View K-Means clustering
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IEEE ACCESS 2025年 13卷 46773-46793页
作者: Yang, Miin-Shen Parveen, Shazia Chung Yuan Christian Univ Dept Appl Math Taoyuan 32023 Taiwan
In machine learning, k-means clustering is an unsupervised leaning technique to partition the data into k clusters that are homogeneous within the cluster and heterogeneous between clusters. The k-means algorithm assi... 详细信息
来源: 评论
Enhanced Vector Quantization for Embedded Machine Learning: A Post-Training Approach With Incremental clustering
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IEEE ACCESS 2025年 13卷 17440-17456页
作者: Flores, Thommas K. S. Medeiros, Morsinaldo Silva, Marianne Costa, Daniel G. Silva, Ivanovitch Univ Fed Rio Grande do Norte PPgEEC BR-59078970 Natal Brazil Univ Fed Alagoas SI BR-57200000 Penedo Brazil Univ Porto Fac Engn SYSTEC ARISE P-4200465 Porto Portugal
TinyML enables the deployment of Machine Learning (ML) models on resource-constrained devices, addressing a growing need for efficient, low-power AI solutions. However, significant challenges remain due to strict memo... 详细信息
来源: 评论
Assessing clustering methods using Shannon's entropy
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INFORMATION SCIENCES 2025年 689卷
作者: Hoayek, Anis Rulliere, Didier Univ Clermont Auvergne Inst Henri Fayol CNRS Mines St EtienneUMR 6158 LIMOS F-42023 St Etienne France
Unsupervised clustering techniques are crucial for effectively partitioning datasets into meaningful subgroups. In this paper, we introduce a novel clustering fuzziness metric based on Shannon's entropy, which qua... 详细信息
来源: 评论