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10.1371/journal.pone.0154446 http://scihub22266oqcxt.onion/10.1371/journal.pone.0154446 C4849582!4849582!27124604     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       PLoS+One 2016 ; 11 (4): ä Nephropedia Template TP {{tp|p=27124604|t=2016. Efficient Record Linkage Algorithms Using Complete Linkage Clustering |pdf=|usr=}}{{27124604}} gab.com Text Efficient Record Linkage Algorithms Using Complete Linkage Clustering JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=27124604 #FOAvip Data from different agencies share data of the same individuals. Linking these datasets to identify all the records belonging to the same individuals is a crucial and challenging problem, especially given the large volumes of data. A large number of available algorithms for record linkage are prone to either time inefficiency or low-accuracy in finding matches and non-matches among the records. In this paper we propose efficient as well as reliable sequential and parallel algorithms for the record linkage problem employing hierarchical clustering methods. We employ complete linkage hierarchical clustering algorithms to address this problem. In addition to hierarchical clustering, we also use two other techniques: elimination of duplicate records and blocking. Our algorithms use sorting as a sub-routine to identify identical copies of records. We have tested our algorithms on datasets with millions of synthetic records. Experimental results show that our algorithms achieve nearly 100% accuracy. Parallel implementations achieve almost linear speedups. Time complexities of these algorithms do not exceed those of previous best-known algorithms. Our proposed algorithms outperform previous best-known algorithms in terms of accuracy consuming reasonable run times. Twit Text FOAVip Efficient Record Linkage Algorithms Using Complete Linkage Clustering ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=27124604 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27124604 #FOAvip English Wikipedia <ref>{{Cite pmid|27124604}}</ref> Efficient Record Linkage Algorithms Using Complete Linkage Clustering #MMPMID27124604 Mamun AA; Aseltine R; Rajasekaran S PLoS One 2016[]; 11 (4): ä PMID27124604show ga Data from different agencies share data of the same individuals. Linking these datasets to identify all the records belonging to the same individuals is a crucial and challenging problem, especially given the large volumes of data. A large number of available algorithms for record linkage are prone to either time inefficiency or low-accuracy in finding matches and non-matches among the records. In this paper we propose efficient as well as reliable sequential and parallel algorithms for the record linkage problem employing hierarchical clustering methods. We employ complete linkage hierarchical clustering algorithms to address this problem. In addition to hierarchical clustering, we also use two other techniques: elimination of duplicate records and blocking. Our algorithms use sorting as a sub-routine to identify identical copies of records. We have tested our algorithms on datasets with millions of synthetic records. Experimental results show that our algorithms achieve nearly 100% accuracy. Parallel implementations achieve almost linear speedups. Time complexities of these algorithms do not exceed those of previous best-known algorithms. Our proposed algorithms outperform previous best-known algorithms in terms of accuracy consuming reasonable run times. äEfficient Record Linkage Algorithms Using Complete Linkage Clustering (epmc).pdf DeepDyve Pubget Overpricing