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A New Approach to Identify High Burnout Medical Staffs by Kernel K-Means Cluster Analysis in a Regional Teaching Hospital in Taiwan [PDF]
Inquiry: The Journal of Health Care Organization, Provision, and Financing, 2016 This study uses kernel k-means cluster analysis to identify medical staffs with high burnout. The data collected in October to November 2014 are from the emotional exhaustion dimension of the Chinese version of Safety Attitudes Questionnaire in a ...
Yii-Ching Lee PhD +3 more
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K-OPLS package: Kernel-based orthogonal projections to latent structures for prediction and interpretation in feature space [PDF]
BMC Bioinformatics, 2008 Background Kernel-based classification and regression methods have been successfully applied to modelling a wide variety of biological data. The Kernel-based Orthogonal Projections to Latent Structures (K-OPLS) method offers unique properties ...
Holmes Elaine +4 more
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Kernel Probabilistic K-Means Clustering
Sensors, 2021 Kernel fuzzy c-means (KFCM) is a significantly improved version of fuzzy c-means (FCM) for processing linearly inseparable datasets. However, for fuzzification parameter m=1, the problem of KFCM (kernel fuzzy c-means) cannot be solved by Lagrangian ...
Bowen Liu +4 more
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Multiple Kernel
IEEE Access, 2021 Multiple kernel clustering algorithms achieve promising performances by exploring the complementary information from kernel matrices corresponding to each data view.
Qiyuan Ou, Long Gao, En Zhu
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Silhouette Analysis for Performance Evaluation in Machine Learning with Applications to Clustering
Entropy, 2021 Grouping the objects based on their similarities is an important common task in machine learning applications. Many clustering methods have been developed, among them k-means based clustering methods have been broadly used and several extensions have ...
Meshal Shutaywi, Nezamoddin N. Kachouie
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Hierarchical Multiple Kernel K-Means Algorithm Based on Sparse Connectivity [PDF]
Jisuanji kexue, 2023 Multiple kernel learning(MKL) aims to find an optimal consistent kernel function.In the hierarchical multiple kernel clustering(HMKC) algorithm,the sample features are extracted layer by layer from high-dimensional space to maximize the retention of ...
WANG Lei, DU Liang, ZHOU Peng
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Jurnal Teknologi Informasi dan Ilmu Komputer, 2021 Merebaknya kasus Covid-19 di Indonesia telah memunculkan berbagai macam topik penelitian yang dilakukan oleh para peneliti di berbagai bidang dan dari bermacam institusi.
Budi Nugroho
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Efficient High-Dimensional Kernel k-Means++ with Random Projection
Applied Sciences, 2021 Using random projection, a method to speed up both kernel k-means and centroid initialization with k-means++ is proposed. We approximate the kernel matrix and distances in a lower-dimensional space Rd before the kernel k-means clustering motivated by ...
Jan Y. K. Chan, Alex Po Leung, Yunbo Xie
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K-MACE and Kernel K-MACE Clustering
IEEE Access, 2020 Determining the correct number of clusters (CNC) is an important task in data clustering and has a critical effect on nalizing the partitioning results. K-means is one of the popular methods of clustering that requires CNC.
Soosan Beheshti +2 more
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The Nyström Kernel Conjugate Gradient Algorithm Based on
IEEE Access, 2020 The kernel conjugate gradient (KCG) algorithms have been proposed to improve the convergence rate and the filtering accuracy of kernel adaptive filters (KAFs) efficiently.
Fuliang He, Kui Xiong, Shiyuan Wang
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