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  • 标题:Knowledge Discovery from Scientometrics Database
  • 本地全文:下载
  • 作者:Muhammad Shaheen ; Maliha Mehmood
  • 期刊名称:Lecture Notes in Engineering and Computer Science
  • 印刷版ISSN:2078-0958
  • 电子版ISSN:2078-0966
  • 出版年度:2019
  • 卷号:2240
  • 页码:156-160
  • 出版社:Newswood and International Association of Engineers
  • 摘要:This paper proposed a unified ranking system for classification of scientific content. A list of parameters for unified ranking of scientific content (journals) is prepared first. A few quality indicators for evaluating the quality of journals are taken from existing parameters which are in use. These indicators include eigen factor, audience factor, impact factor, article influence, and citations. We also proposed one new metric, prestige of journal (PoJ) for the evaluation of journals. The values of different journals for the proposed indicators including the newer one is stored in an integrated database. A popular data mining technique for unsupervised classification named K Means clustering is applied to group the journals in different clusters. Clustering is an unsupervised classification technique in which the un-clustered classes do not bear any label. The clusters are labeled to find the exact rank of a science journal by using a state of the art technique of labeling clusters developed by the author of this paper. The experimental design of the paper is done and the parameters to evaluate the experiment are finalized. The results obtained from different experiments are in process yet and will be published in the extended version of this paper.
  • 关键词:Data mining; Scientometrics; Impact factor; Classification; Clustering;
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