{"action":"opened","issue":{"url":"https://api.github.com/repos/ivypublisher/Scientific-Journal-of-Information-Engineering/issues/95","repository_url":"https://api.github.com/repos/ivypublisher/Scientific-Journal-of-Information-Engineering","labels_url":"https://api.github.com/repos/ivypublisher/Scientific-Journal-of-Information-Engineering/issues/95/labels{/name}","comments_url":"https://api.github.com/repos/ivypublisher/Scientific-Journal-of-Information-Engineering/issues/95/comments","events_url":"https://api.github.com/repos/ivypublisher/Scientific-Journal-of-Information-Engineering/issues/95/events","html_url":"https://github.com/ivypublisher/Scientific-Journal-of-Information-Engineering/issues/95","id":166961732,"number":95,"title":"An Approach to Subject Database Planning Based on K-means","user":{"login":"ivypublisher","id":6970392,"avatar_url":"https://avatars.githubusercontent.com/u/6970392?v=3","gravatar_id":"","url":"https://api.github.com/users/ivypublisher","html_url":"https://github.com/ivypublisher","followers_url":"https://api.github.com/users/ivypublisher/followers","following_url":"https://api.github.com/users/ivypublisher/following{/other_user}","gists_url":"https://api.github.com/users/ivypublisher/gists{/gist_id}","starred_url":"https://api.github.com/users/ivypublisher/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/ivypublisher/subscriptions","organizations_url":"https://api.github.com/users/ivypublisher/orgs","repos_url":"https://api.github.com/users/ivypublisher/repos","events_url":"https://api.github.com/users/ivypublisher/events{/privacy}","received_events_url":"https://api.github.com/users/ivypublisher/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2016-07-22T03:06:54Z","updated_at":"2016-07-22T03:06:54Z","closed_at":null,"body":"Author: Qinglan Fan, Lin Wang, Yaqing Liu, Dun Bai, Mingyu Lu\r\nAbstract: Subject database planning is always the emphasis of information resource planning. Algorithm for entities aggregation has heavy impact on the quality of subject database planning. However, the existing approaches to entities aggregation computation are inclined to fall into cluster offset, which does great harm to the quality of subject database planning. Against the problem, we firstly calculate the degree of aggregation between entities. Secondly, we view the relations of aggregation as the relations of links between web pages. We apply PageRank algorithm to sort all entity pairs by importance. At last, we exploit K-means algorithm to aggregate entities iteratively. The results of experiments show that our approach avoids cluster offset and improves the quality of subject database planning.\r\nKeywords: Subject Database; Information Resource Planning; Cluster Offset\r\nhttp://www.sjie.org/paperinfo/28082.shtml"}}
{"actor":{"display_login":"ivypublisher"}}