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10.1155/2016/7386517 http://scihub22266oqcxt.onion/10.1155/2016/7386517 C4897663!4897663 !27313603     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=27313603 &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       Comput+Intell+Neurosci 2016 ; 2016 (?): 7386517 Nephropedia Template TP {{tp|p=27313603 |t=2016. Stratification-Based Outlier Detection over the Deep Web |pdf=|usr=}}{{27313603 }} gab.com Text Stratification-Based Outlier Detection over the Deep Web JO: Comput Intell Neurosci http://www.kidney.de/pget.php?&tw=1&pmid=27313603 #FOAvip For many applications, finding rare instances or outliers can be more interesting than finding common patterns. Existing work in outlier detection never considers the context of deep web. In this paper, we argue that, for many scenarios, it is more meaningful to detect outliers over deep web. In the context of deep web, users must submit queries through a query interface to retrieve corresponding data. Therefore, traditional data mining methods cannot be directly applied. The primary contribution of this paper is to develop a new data mining method for outlier detection over deep web. In our approach, the query space of a deep web data source is stratified based on a pilot sample. Neighborhood sampling and uncertainty sampling are developed in this paper with the goal of improving recall and precision based on stratification. Finally, a careful performance evaluation of our algorithm confirms that our approach can effectively detect outliers in deep web. Twit Text FOAVip Stratification-Based Outlier Detection over the Deep Web ????Comput Intell Neurosci http://www.kidney.de/pget.php?&tw=1&pmid=27313603 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27313603 #FOAvip English Wikipedia <ref>{{Cite pmid|27313603 }}</ref> Stratification-Based Outlier Detection over the Deep Web #MMPMID27313603 Xian X ; Zhao P ; Sheng VS ; Fang L ; Gu C ; Yang Y ; Cui Z Comput Intell Neurosci 2016[]; 2016 (?): 7386517 PMID27313603 show ga For many applications, finding rare instances or outliers can be more interesting than finding common patterns. Existing work in outlier detection never considers the context of deep web. In this paper, we argue that, for many scenarios, it is more meaningful to detect outliers over deep web. In the context of deep web, users must submit queries through a query interface to retrieve corresponding data. Therefore, traditional data mining methods cannot be directly applied. The primary contribution of this paper is to develop a new data mining method for outlier detection over deep web. In our approach, the query space of a deep web data source is stratified based on a pilot sample. Neighborhood sampling and uncertainty sampling are developed in this paper with the goal of improving recall and precision based on stratification. Finally, a careful performance evaluation of our algorithm confirms that our approach can effectively detect outliers in deep web. |*Algorithms [MESH] |*Information Storage and Retrieval [MESH] |*Internet [MESH] |Commerce [MESH] |Database Management Systems [MESH]Stratification-Based Outlier Detection over the Deep Web (epmc).pdf DeepDyve Pubget Overpricing