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10.1038/s41598-021-93244-2 http://scihub22266oqcxt.onion/10.1038/s41598-021-93244-2 34244541!8271010!34244541     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Sci+Rep 2021 ; 11 (1): 14214 Nephropedia Template TP {{tp|p=34244541|t=2021. A novel bidirectional clustering algorithm based on local density |pdf=|usr=}}{{34244541}} gab.com Text A novel bidirectional clustering algorithm based on local density JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=34244541 #FOAvip With the widely application of cluster analysis, the number of clusters is gradually increasing, as is the difficulty in selecting the judgment indicators of cluster numbers. Also, small clusters are crucial to discovering the extreme characteristics of data samples, but current clustering algorithms focus mainly on analyzing large clusters. In this paper, a bidirectional clustering algorithm based on local density (BCALoD) is proposed. BCALoD establishes the connection between data points based on local density, can automatically determine the number of clusters, is more sensitive to small clusters, and can reduce the adjusted parameters to a minimum. On the basis of the robustness of cluster number to noise, a denoising method suitable for BCALoD is proposed. Different cutoff distance and cutoff density are assigned to each data cluster, which results in improved clustering performance. Clustering ability of BCALoD is verified by randomly generated datasets and city light satellite images. Twit Text FOAVip A novel bidirectional clustering algorithm based on local density ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=34244541 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34244541 #FOAvip English Wikipedia <ref>{{Cite pmid|34244541}}</ref> A novel bidirectional clustering algorithm based on local density #MMPMID34244541 Lyu B; Wu W; Hu Z Sci Rep 2021[Jul]; 11 (1): 14214 PMID34244541show ga With the widely application of cluster analysis, the number of clusters is gradually increasing, as is the difficulty in selecting the judgment indicators of cluster numbers. Also, small clusters are crucial to discovering the extreme characteristics of data samples, but current clustering algorithms focus mainly on analyzing large clusters. In this paper, a bidirectional clustering algorithm based on local density (BCALoD) is proposed. BCALoD establishes the connection between data points based on local density, can automatically determine the number of clusters, is more sensitive to small clusters, and can reduce the adjusted parameters to a minimum. On the basis of the robustness of cluster number to noise, a denoising method suitable for BCALoD is proposed. Different cutoff distance and cutoff density are assigned to each data cluster, which results in improved clustering performance. Clustering ability of BCALoD is verified by randomly generated datasets and city light satellite images. äA novel bidirectional clustering algorithm based on local density (epmc).pdf DeepDyve Pubget Overpricing