Data mining-based optimal assignment of apparel size for mass customization by Zhujun Wang, Cheng Chi, Mengyun Zhang, Xianyi Zeng, Pascal Bruniaux, Jianping Wang, Yingmei Xing

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Feasibility of U-Net model for cerebral arteries segmentation with low-dose computed tomography angiographic images with pre-processing methods by Seong-Hyeon Kang, Kyuseok Kim, Jina Shim, Youngjin Lee

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Physical Health Portrait and Intervention Strategy of College Students Based on Multivariate Cluster Analysis and Machine Learning by Rong Guo, Rou Dong, Ni Lu, Lin Yu, Chaoxian Chen, Yonglin Che, Jiajin Zhang, Jianke Yang

Subjects: “…k-means algorithm…”
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Clustering Iranian women according to their menopausal severity symptoms and exploring the factors associated with severe categories, using baseline category logit model by Fahimeh Hoseinzadeh, Habibollah Esmaily, Sedigheh Ayatiafin, Azadeh Saki

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Three-Dimensional Trajectory Optimization for UAV-Based Post-Disaster Data Collection by Renkai Zhao, Gia Khanh Tran

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Detecting Short-Notice Cancellation in Hotels with Machine Learning by Eleazar C-Sánchez, Agustín J. Sánchez-Medina

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Risk assessment of corn borer based on feature optimization and weighted spatial clustering: a case study in Shandong Province, China by Yanan Zuo, Min Ji, Jiutao Yang, Zhenjin Li, Jing Wang

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Determining Energy Production and Consumption Signatures Using Unsupervised Clustering by Andrzej Marciniak, Arkadiusz Małek

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Improved Kalman Filtering Algorithm Based on Levenberg–Marquart Algorithm in Ultra-Wideband Indoor Positioning by Changping Xie, Xinjian Fang, Xu Yang

“…To improve the current indoor positioning algorithms, which have insufficient positioning accuracy, an ultra-wideband (UWB) positioning algorithm based on the Levenberg–Marquardt algorithm with improved Kalman filtering is proposed. …”
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k-medianoids Clustering Algorithm by James Cha, Teryn Cha, Sung-Hyuk Cha

“…One of the simplest and popular clustering method is the simple k-means clustering algorithm. One of the drawbacks of the method is its sensitivity to outliers. …”
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An algorithm for forensic toolmark comparisons by Maria Cuellar, Sheng Gao, Heike Hofmann

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Quantum Algorithms for the Pathwise Lasso by Joao F. Doriguello, Debbie Lim, Chi Seng Pun, Patrick Rebentrost, Tushar Vaidya

“…We present a novel quantum high-dimensional linear regression algorithm with an $\ell_1$-penalty based on the classical LARS (Least Angle Regression) pathwise algorithm. …”
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An Improvement of Stochastic Gradient Descent Approach for Mean-Variance Portfolio Optimization Problem by Stephanie S. W. Su, Sie Long Kek

“…Furthermore, the applicability of SGD, Adam, AdaMax, Nadam, AMSGrad, and AdamSE algorithms in solving the mean-variance portfolio optimization problem is validated.…”
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Prediction of dam deformation using adaptive noise CEEMDAN and BiGRU time series modeling by WANG Zixuan, OU Bin, CHEN Dehui, YANG Shiyong, ZHAO Dingzhu, FU Shuyan

Subjects: “…dam deformation; complete ensemble empirical mode decomposition of adaptive noise; sample entropy reconstruction; k-means clustering algorithm; symbiotic search algorithm; variational mode decomposition…”
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Novel validity indices for dynamic clustering and an Improved Dynamic Fuzzy C-Means by Ramiro Saltos, Ignacio Carvajal, Fernando Crespo, Richard Weber

“…To illustrate the application of these novel indices, we introduce an improved version of the dynamic fuzzy c-means algorithm (I-DFCM) which offers enhanced computational stability for handling dynamic data. …”
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Penerapan Metode K-Means Berbasis Jarak untuk Deteksi Kendaraan Bergerak by Yuslena Sari, Andreyan Rizky Baskara, Puguh Budi Prakoso

“…In this paper, the K-Means algorithm applies Euclidean distance, Manhattan distance, Canberra distance, Chebyshev distance and Braycurtis distance. …”
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Evapotranspiration Prediction Method Based on K-Means Clustering and QPSO-MKELM Model by Chuansheng Zhang, Minglai Yang

“…Ablation experiment results show that introducing K-means clustering improves the model’s running speed, while the improved QPSO algorithm and the introduction of multiple kernel functions enhance the model’s accuracy. …”
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K-Means Based Bee Colony Optimization for Clustering in Heterogeneous Sensor Network by Prince Modey, Gaddafi Abdul-Salaam, Emmanuel Freeman, Patrick Acheampong, William Leslie Brown-Acquaye, Israel Edem Agbehadji, Richard C. Millham

“…This study proposes a Bee Colony Optimization that synergistically combines K-mean algorithms (referred to as K-BCO) for efficient clustering in heterogeneous sensor networks. …”
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Substructure correlation adaptation transfer learning method based on K-means clustering by Haoshuang LIU, Yong ZHANG, Yingbo CAO

“…Domain drifts severely affect the performance of traditional machine learning methods, and existing domain adaptive methods are mainly represented by adaptive adjustment cross-domain through global, class-level, or sample-level distribution adaptation.However, too coarse global matching and class-level matching can lead to insufficient adaptation, and sample-level adaptation to noise can lead to excessive adaptation.A substructure correlation adaptation (SCOAD) transfer learning algorithm based on K-means clustering was proposed.Firstly, multiple subdomains of the source domain and the target domain were obtained by K-means clustering.Then, the matching of the second-order statistics of the subdomain center was sought.Finally, the target domain samples were classified by using the subdomain structure.The proposed method approach further improves the performance of knowledge transfer between the source and target domains on top of the traditional approach.Experimental results on common transfer learning datasets show the effectiveness of the proposed method.…”
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Substructure correlation adaptation transfer learning method based on K-means clustering by Haoshuang LIU, Yong ZHANG, Yingbo CAO

“…Domain drifts severely affect the performance of traditional machine learning methods, and existing domain adaptive methods are mainly represented by adaptive adjustment cross-domain through global, class-level, or sample-level distribution adaptation.However, too coarse global matching and class-level matching can lead to insufficient adaptation, and sample-level adaptation to noise can lead to excessive adaptation.A substructure correlation adaptation (SCOAD) transfer learning algorithm based on K-means clustering was proposed.Firstly, multiple subdomains of the source domain and the target domain were obtained by K-means clustering.Then, the matching of the second-order statistics of the subdomain center was sought.Finally, the target domain samples were classified by using the subdomain structure.The proposed method approach further improves the performance of knowledge transfer between the source and target domains on top of the traditional approach.Experimental results on common transfer learning datasets show the effectiveness of the proposed method.…”
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