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QFINCH: Quick hierarchical clustering using k-means and first neighbor relations Conference paper
Zhang, Jiajun, Yang, Geping, Yang, Yiyang, Lu, Juan. QFINCH: Quick hierarchical clustering using k-means and first neighbor relations[C]:Institute of Electrical and Electronics Engineers Inc., 2024, 1992-1997.
Authors:  Zhang, Jiajun;  Yang, Geping;  Yang, Yiyang;  Lu, Juan
Favorite | TC[Scopus]:0 | Submit date：2024/08/05
Data Mining  First Neighbor  Hierarchical Clustering  K-means  
Comparative Analysis of Offshore Wind Power Prediction Models and Clustering-Based Daily Output Classification Conference paper
Ou, Zhongxi, Lan, Wei, Zhang, Liang, Tong, Zhu, Liu, Dundun, Liu, Zhaoxi. Comparative Analysis of Offshore Wind Power Prediction Models and Clustering-Based Daily Output Classification[C]:Institute of Electrical and Electronics Engineers Inc., 2024, 1482-1487.
Authors:  Ou, Zhongxi;  Lan, Wei;  Zhang, Liang;  Tong, Zhu;  Liu, Dundun; et al.
Favorite | TC[Scopus]:1 | Submit date：2024/09/03
Gru  K-means Clustering  Lstm  Offshore Wind Power  Offshore Wind Power Forecasting  Prediction Accuracy  Prediction Models  
Self-Adaptive Multiprototype-Based Competitive Learning Approach: A k-Means-Type Algorithm for Imbalanced Data Clustering Journal article
Lu, Yang, Cheung, Yiu Ming, Tang, Yuan Yan. Self-Adaptive Multiprototype-Based Competitive Learning Approach: A k-Means-Type Algorithm for Imbalanced Data Clustering[J]. IEEE Transactions on Cybernetics, 2021, 51(3), 1598-1612.
Authors:  Lu, Yang;  Cheung, Yiu Ming;  Tang, Yuan Yan
Favorite | TC[WOS]:40 TC[Scopus]:51  IF:9.4/10.3 | Submit date：2021/12/07
Class Imbalance Learning  Competitive Learning  Data Clustering  Internal Validation Measure  K-means-type Algorithm  Multiprototype Clustering  
Abnormal dynamic functional connectivity and brain states in Alzheimer's diseases: functional near-infrared spectroscopy study Journal article
Haijing Niu, Zhaojun Zhu, Mengjing Wang, Xuanyu Li, Zhen Yuan, Yu Sun, Ying Han. Abnormal dynamic functional connectivity and brain states in Alzheimer's diseases: functional near-infrared spectroscopy study[J]. NEUROPHOTONICS, 2019, 6(2).
Authors:  Haijing Niu;  Zhaojun Zhu;  Mengjing Wang;  Xuanyu Li;  Zhen Yuan; et al.
Favorite | TC[WOS]:32 TC[Scopus]:32  IF:4.8/4.3 | Submit date：2020/06/24
Resting State  Functional Connectivity  Sliding-window  K-means Clustering  Brain Network  Alzheimer's Disease  
Abnormal dynamic functional connectivity and brain states in Alzheimer’s diseases: functional near-infrared spectroscopy study Journal article
Haijing Niu, Zhaojun Zhu, Mengjing Wan, Xuanyu Li, Zhen Yuan, Yu Sun, Ying Han. Abnormal dynamic functional connectivity and brain states in Alzheimer’s diseases: functional near-infrared spectroscopy study[J]. Neurophotonics, 2019, 6(2), 025010.
Authors:  Haijing Niu;  Zhaojun Zhu;  Mengjing Wan;  Xuanyu Li;  Zhen Yuan; et al.
Adobe PDF | Favorite | TC[WOS]:32 TC[Scopus]:32 | Submit date：2022/08/21
CommunicAtion WithIn The BraIn Is Highly Dynamic. AlzheI.e.’s dIseAse (Ad) ExhibIts Dynamic.progression COrrespondIng To a DeclIne In MemOry And Cognition. HoWever, Little Is Known Of wheTher BraIn Dynamic. Are dIsrupted In Ad And Its Prodromal Stage, Mild CognitI.e.impairment (Mci). FOr Our Study, We AcquI.e. High samplIng RAte Functional near-InfrAred Spectroscopy imagIng DAta At Rest From The EntI.e.cOrtex Of 23 pAtients With Ad Dementia, 25 pAtients With Amnestic Mild CognitI.e.impairment (aMci), And 30 age-mAtched Healthy Controls (Hcs). slidIng-wIndow cOrrelAtion And K-means clusterIng Analyses Were Used To Construct Dynamic.Functional Connectivity (Fc) Maps FOr Each Participant. We dIscovered thAt The BraIn’s Dynamic.Fc Variability Strength (q) Significantly IncreAsed In Both aMci And Ad Group As compAred To Hcs. usIng The q Value As a meAsurement, The clAssificAtion perFOrmance ExhibI.e. a Good poWer In differentiAtIng aMci [Area Under The Curve (Auc ¼ 82.5%)] Or Ad (Auc ¼ 86.4%) From Hcs. furThermOre, We Identified Two abnOrmal BraIn Fc stAtes In The Ad Group, Of Which The Occurrence Frequency (f) ExhibI.e. a Significant decreAse FOr The First-level Fc stAte (stAte 1) And a Significant IncreAse FOr The Second-level Fc stAte (stAte 2). We Also Found thAt The abnOrmal f In These Two stAtes Significantly cOrrelAted With The CognitI.e.impairment In pAtients. These fIndIngs provI.e.The First EvI.e.ce To demonstRAte The dIsruptions Of Dynamic.BraIn Connectivity In aMci And Ad And Extend The trAditional stAtic (I.e., tI.e.averaged) Fc fIndIngs In The dIseAse (I.e., dIsconnection Syndrome) And Thus provI.e.Insights InTo UnderstAndIng The pAthophysiological mechanIsms occurrIng In aMci And Ad.  
Data Clustering Using the Cooperative Search Based Artificial Bee Colony Algorithm Conference paper
Guo, Chen, Tang, Heng, Lee, Chang Boon, Niu, Ben. Data Clustering Using the Cooperative Search Based Artificial Bee Colony Algorithm[C], 2019, 660-671.
Authors:  Guo, Chen;  Tang, Heng;  Lee, Chang Boon;  Niu, Ben
Favorite | TC[Scopus]:1 | Submit date：2019/11/27
Data Clustering  K-means  Artificial Bee Colony Algorithm  
Grouping Users Using a Combination-Based Clustering Algorithm in the Service Environment Conference paper
Yan Wang, Jiantao Zhou, Xinyuan LI, Xiaoyu Song. Grouping Users Using a Combination-Based Clustering Algorithm in the Service Environment[C], 2017, 721-727.
Authors:  Yan Wang;  Jiantao Zhou;  Xinyuan LI;  Xiaoyu Song
Favorite | TC[WOS]:1 TC[Scopus]:4 | Submit date：2018/12/22
Affinity Propagation  Clustering Algorithm  Group Technology  K-means  Mapreduce  
Extending the grenade explosion approach for effective clustering Conference paper
Mojgan Ghanavati, Raymond K. Wong, Simon Fong, Mohammad Reza Gholamian. Extending the grenade explosion approach for effective clustering[C]:IEEE, 2016, 28-35.
Authors:  Mojgan Ghanavati;  Raymond K. Wong;  Simon Fong;  Mohammad Reza Gholamian
Favorite | TC[Scopus]:2 | Submit date：2019/02/13
Clustering  K-means  Grenade Explosion Method  
Integrating Nature-inspired Optimization Algorithms to K-means Clustering Conference paper
Rui Tang, Simon Fong, Xin-She Yang, Suash Deb. Integrating Nature-inspired Optimization Algorithms to K-means Clustering[C]:IEEE, 2012, 116-123.
Authors:  Rui Tang;  Simon Fong;  Xin-She Yang;  Suash Deb
Favorite | TC[Scopus]:77 | Submit date：2019/02/13
K-means Clustering Algorithm  Wolf Search Optimization  Firefly Optimization  Cuckoo Optimization  Bat Optimization  Ant Colony Optimization