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10.1016/j.spasta.2021.100518 http://scihub22266oqcxt.onion/10.1016/j.spasta.2021.100518 34026473!8123527!34026473     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=34026473&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Spat+Stat 2022 ; 49 (ä): 100518 Nephropedia Template TP {{tp|p=34026473|t=2022. Spatial robust fuzzy clustering of COVID 19 time series based on B-splines |pdf=|usr=}}{{34026473}} gab.com Text Spatial robust fuzzy clustering of COVID 19 time series based on B-splines JO: Spat Stat http://www.kidney.de/pget.php?&tw=1&pmid=34026473 #FOAvip The aim of the work is to identify a clustering structure for the 20 Italian regions according to the main variables related to COVID-19 pandemic. Data are observed over time, spanning from the last week of February 2020 to the first week of February 2021. Dealing with geographical units observed at several time occasions, the proposed fuzzy clustering model embedded both space and time information. Properly, an Exponential distance-based Fuzzy Partitioning Around Medoids algorithm with spatial penalty term has been proposed to classify the spline representation of the time trajectories. The results show that the heterogeneity among regions along with the spatial contiguity is essential to understand the spread of the pandemic and to design effective policies to mitigate the effects. Twit Text FOAVip Spatial robust fuzzy clustering of COVID 19 time series based on B-splines ????Spat Stat http://www.kidney.de/pget.php?&tw=1&pmid=34026473 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34026473 #FOAvip English Wikipedia <ref>{{Cite pmid|34026473}}</ref> Spatial robust fuzzy clustering of COVID 19 time series based on B-splines #MMPMID34026473 D'Urso P; De Giovanni L; Vitale V Spat Stat 2022[Jun]; 49 (ä): 100518 PMID34026473show ga The aim of the work is to identify a clustering structure for the 20 Italian regions according to the main variables related to COVID-19 pandemic. Data are observed over time, spanning from the last week of February 2020 to the first week of February 2021. Dealing with geographical units observed at several time occasions, the proposed fuzzy clustering model embedded both space and time information. Properly, an Exponential distance-based Fuzzy Partitioning Around Medoids algorithm with spatial penalty term has been proposed to classify the spline representation of the time trajectories. The results show that the heterogeneity among regions along with the spatial contiguity is essential to understand the spread of the pandemic and to design effective policies to mitigate the effects. äSpatial robust fuzzy clustering of COVID 19 time series based on B-spl(epmc).pdf DeepDyve Pubget Overpricing