Suchergebnisse - K‐means cluster algorithm parameter determination

Urban storm flood simulation using improved SWMM based on K‐means clustering of parameter samples von Sun, Yue, Liu, Chengshuai, Du, Xian, Yang, Fan, Yao, Yichen, Soomro, Shan‐e‐hyder, Hu, Caihong

“… Calibrated uncertain parameters from 76 papers were selected as samples, and the K‐means clustering algorithm was used to cluster and calculate the parameter values …”
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Determination of the appropriate parameters for K‐means clustering using selection of region clusters based on density DBSCAN (SRCD‐DBSCAN) von Limwattanapibool, Onapa, Arch‐int, Somjit

“… An inappropriate determination of the number of clusters or the initial cluster centre decreases the accuracy of K‐means clustering …”
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Search Space Reduction for Determination of Earthquake Source Parameters Using PCA and k-Means Clustering von Lee, Seongjae, Kim, Taehyoun

“… The characteristics of an earthquake can be derived by estimating the source geometries of the earthquake using parameter inversion that minimizes the L2 norm of residuals between the measured …”
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Enhanced Multi‐Objective Optimization Model for Bridge Performance Assessment and Prediction, Based on Improved PCA, K‐Means Clustering, and Kaplan–Meier Survival Algorithm von Gui, Chengzhong, Duan, Zhi, Huang, Zuwei, Sun, Zhiguo, Qiao, Wei, Cheng, Yu

“… ‐source heterogeneous data, selection of key sub‐parameters using Principal Component Analysis (PCA), enhanced K …”
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Research on Urban Storm Flood Simulation by Coupling K-means Machine Learning Algorithm and GIS Spatial Analysis Technology into SWMM Model von Liu, Chengshuai, Hu, Caihong, Zhao, Chenchen, Sun, Yue, Xie, Tianning, Wang, Huiliang

“… The K-means clustering machine learning algorithm is used to determine the uncertain parameters of the SWMM model, while GIS spatial analysis techniques enhance the two-dimensional realism of flood simulation …”
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Determination of Customer Satisfaction using Improved K-means algorithm von Zare, Hamed, Emadi, Sima

“… ). To accurately predict customer’s behaviour, clustering, especially K -means, is one of the most important data mining techniques used in customer relationship management marketing, with which it is possible to identify customers …”
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Open cluster membership probability based on K-means clustering algorithm von El Aziz, Mohamed Abd, Selim, I. M., Essam, A.

“… So in this paper, we presented a new method for the determination of open cluster membership based on K-means clustering algorithm …”
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Determination of impact fragments from particle analysis via smoothed particle hydrodynamics and k-means clustering von Sakong, Jae, Woo, Sung-Choong, Kim, Tae-Won

“… •A method to determine the fragment distribution from the particle dispersion by using a clustering algorithm was suggested …”
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Comparison of K-Means and Fuzzy c-Means Algorithm Performance for Automated Determination of the Arterial Input Function von Yin, Jiandong, Sun, Hongzan, Yang, Jiawen, Guo, Qiyong

“… Two automatic methods have been reported that are based on two frequently used clustering algorithms: fuzzy c-means (FCM) and K-means …”
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Large-Scale Automatic K-Means Clustering for Heterogeneous Many-Core Supercomputer von Yu, Teng, Zhao, Wenlai, Liu, Pan, Janjic, Vladimir, Yan, Xiaohan, Wang, Shicai, Fu, Haohuan, Yang, Guangwen, Thomson, John

“… Furthermore, we propose an automatic hyper-parameter determination process for k-means clustering, by automatically generating and executing the clustering …”
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Non-hierarchical cluster analysis for determination of resistance to worm infection in meat sheep von Araujo, Johnny Iglesias Mendes, da Silva Santos, Natanael Pereira, de Oliveira, Max Brandão, Sena, Luciano Silva, Biagiotti, Daniel, de Araujo Rego Neto, Aurino, Sarmento, José Lindenberg Rocha

“… Inês sheep by combining different sets of gastrointestinal parasite resistance indicator traits, using the k -means algorithm …”
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Combining data-intelligent algorithms for the assessment and predictive modeling of groundwater resources quality in parts of southeastern Nigeria von Egbueri, Johnbosco C., Agbasi, Johnson C.

“… Machine learning algorithms have proven useful in the estimation, classification, and prediction of water quality parameters …”
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Unsupervised machine learning effectively clusters pediatric spastic cerebral palsy patients for determination of optimal responders to selective dorsal rhizotomy von Hou, Xiaobin, Yan, Yanyun, Zhan, Qijia, Wang, Junlu, Xiao, Bo, Jiang, Wenbin

“… Spasticity of lower limbs, the number of target muscles, motor functions, and other clinical parameters were used as input variables for unsupervised machine learning to cluster all included patients …”
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Determination of essential phenotypic elements of clusters in high-dimensional entities—DEPECHE von Theorell, Axel, Bryceson, Yenan Troi, Theorell, Jakob

“… complex data interpretable. Here, we introduce DEPECHE, a rapid, parameter free, sparse k-means-based algorithm for clustering of multi- and megavariate single-cell data …”
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A Hybrid-Weight TOPSIS and Clustering Approach for Optimal GNSS Station Selection in Multi-GNSS Precise Orbit Determination von Jin, Weitong, Li, Xing, Chen, Liang, Sheng, Chuanzhen, Yuan, Yongqiang, Zhang, Keke, Li, Xingxing, Zhang, Jingkui, Zhang, Xulun, Yu, Baoguo

“… ) model with spherical k-means clustering, effectively resolving the challenge of balancing station data quality with uniform spatial distribution …”
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Determination of Interrupt-Coalescence Latency of Remote Hosts Through Active Measurement von Salehin, Khondaker, Sahasrabudhe, Vinitmadhukar, Rojas-Cessa, Roberto

“… Even though the adoption of IC has its benefits, the additional delay negatively affects the hosts that are involved in the performance measurement of various network parameters and time-sensitive applications …”
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An Adaptive Parameter-Free Optimal Number of Market Segments Estimation Algorithm Based on a New Internal Validity Index von Qi, Jianfang, Li, Yue, Jin, Haibin, Feng, Jianying, Tian, Dong, Mu, Weisong

“… ) Between-Within-Connectivity (BWCON) and a new stable clustering algorithm Natural-SDK-means++ (NSDK-means++) in a novel way. First, to complete the evaluation dimensions …”
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Determination of cluster number in clustering microarray data von Shen, Judong, Chang, Shing I., Lee, E. Stanley, Deng, Youping, Brown, Susan J.

“… Although various algorithms have been proposed for the clustering of microarray data, the main difficulty remains to be the determination of the optimal number of clusters …”
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Analysis of the mandibular canal course using unsupervised machine learning algorithm von Kim, Young Hyun, Jeon, Kug Jin, Lee, Chena, Choi, Yoon Joo, Jung, Hoi-In, Han, Sang-Sun

“… Cluster analysis was carried out as follows: parameter measurement, parameter normalization, cluster tendency evaluation, optimal number of clusters determination, and k-means cluster analysis …”
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Fuzzy K-Means Clustering With Discriminative Embedding von Nie, Feiping, Zhao, Xiaowei, Wang, Rong, Li, Xuelong, Li, Zhihui

“… Fuzzy K-Means (FKM) clustering is of great importance for analyzing unlabeled data …”
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