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10.1371/journal.pone.0187164 http://scihub22266oqcxt.onion/10.1371/journal.pone.0187164 C5659815!5659815 !29077747     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=29077747 &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       PLoS+One 2017 ; 12 (10 ): e0187164 Nephropedia Template TP {{tp|p=29077747 |t=2017. Hybrid self-optimized clustering model based on citation links and textual features to detect research topics |pdf=|usr=}}{{29077747 }} gab.com Text Hybrid self-optimized clustering model based on citation links and textual features to detect research topics JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=29077747 #FOAvip The challenge of detecting research topics in a specific research field has attracted attention from researchers in the bibliometrics community. In this study, to solve two problems of clustering papers, i.e., the influence of different distributions of citation links and involved textual features on similarity computation, the authors propose a hybrid self-optimized clustering model to detect research topics by extending the hybrid clustering model to identify "core documents". First, the Amsler network, consisting of bibliographic coupling and co-citation links, is created to calculate the citation-based similarity based on the cosine angle of papers. Second, the cosine similarity is also used to compute the text-based similarity, which consists of the textual statistical and topological features. Then, the cosine angle of the linear combination of citation- and text-based similarity is considered as the hybrid similarity. Finally, the Louvain method is applied to cluster papers, and the terms based on term frequency are used to label clusters. To test the performance of the proposed model, a dataset related to the data envelopment analysis field is used for comparison and analysis of clustering results. Based on the benchmark built, different clustering methods with different citation links or textual features are compared according to evaluation measures. The results show that the proposed model can obtain reasonable and effective clustering results, and the research topics of data envelopment analysis field are also analyzed based on the proposed model. As different features are considered in the proposed model compared with previous hybrid clustering models, the proposed clustering model can provide inspiration for further studies on topic identification by other researchers. Twit Text FOAVip Hybrid self-optimized clustering model based on citation links and textual features to detect research topics ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=29077747 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29077747 #FOAvip English Wikipedia <ref>{{Cite pmid|29077747 }}</ref> Hybrid self-optimized clustering model based on citation links and textual features to detect research topics #MMPMID29077747 Yu D ; Wang W ; Zhang S ; Zhang W ; Liu R PLoS One 2017[]; 12 (10 ): e0187164 PMID29077747 show ga The challenge of detecting research topics in a specific research field has attracted attention from researchers in the bibliometrics community. In this study, to solve two problems of clustering papers, i.e., the influence of different distributions of citation links and involved textual features on similarity computation, the authors propose a hybrid self-optimized clustering model to detect research topics by extending the hybrid clustering model to identify "core documents". First, the Amsler network, consisting of bibliographic coupling and co-citation links, is created to calculate the citation-based similarity based on the cosine angle of papers. Second, the cosine similarity is also used to compute the text-based similarity, which consists of the textual statistical and topological features. Then, the cosine angle of the linear combination of citation- and text-based similarity is considered as the hybrid similarity. Finally, the Louvain method is applied to cluster papers, and the terms based on term frequency are used to label clusters. To test the performance of the proposed model, a dataset related to the data envelopment analysis field is used for comparison and analysis of clustering results. Based on the benchmark built, different clustering methods with different citation links or textual features are compared according to evaluation measures. The results show that the proposed model can obtain reasonable and effective clustering results, and the research topics of data envelopment analysis field are also analyzed based on the proposed model. As different features are considered in the proposed model compared with previous hybrid clustering models, the proposed clustering model can provide inspiration for further studies on topic identification by other researchers. |*Cluster Analysis [MESH]Hybrid self-optimized clustering model based on citation links and tex(epmc).pdf DeepDyve Pubget Overpricing