首页    期刊浏览 2025年02月27日 星期四
登录注册

文章基本信息

  • 标题:Dimensionality Reduction Methods: The Comparison Of Speed And Accuracy
  • 本地全文:下载
  • 作者:Jelena Zubova ; Olga Kurasova ; Marius Liutvinavičius
  • 期刊名称:Public Policy And Administration
  • 印刷版ISSN:2029-2872
  • 出版年度:2018
  • 卷号:47
  • 期号:1
  • 页码:151-160
  • DOI:10.5755/j01.itc.47.1.18813
  • 语种:English
  • 出版社:Kaunas University of Technology
  • 摘要:This research focuses on big data visualization that is based on dimensionality reduction methods. We propose a multi-level method for data clustering and visualization. Whole data mining process is divided into separate steps. For each step particular dimensionality reduction and visualization method is applied considering to data volume and type. The selection of methods is based on their speed and accuracy. Therefore the comparison of the selected methods is made according to these two criteria. Three groups of datasets containing different kind of data are used for methods evaluation. The factors that influence speed or accuracy are determined. The rank of investigated methods based on research results is presented in this paper.
  • 关键词:big data;dimensionality reduction;data visualization
国家哲学社会科学文献中心版权所有