在一个大型科学库中探索相似性模式
Daniel Witschard1, Ilir Jusufi2, Kostiantyn Kucher3
1Department of Computer Science and Media Technology, Linnaeus University, Växjö, Sweden.
PloS one
|April 21, 2025
概括
本研究引入了一种新的相似性分析方法,将相似性视为关系和属性. 这种方法通过揭示相似性标准如何影响结果和项目连接来增强数据探索.
科学领域:
- 数据科学数据科学数据科学
- 信息可视化 信息可视化
- 图书统计学 图书统计学
背景情况:
- 基于相似性的分析对于探索大型数据集和发现内在数据结构至关重要.
- 目前的方法,如聚类和缩小维度,往往无法解决相似性标准如何影响分析结果.
- 在可以动态探索相似性标准变化如何影响数据关系的方法中存在差距.
研究的目的:
- 为基于相似性的分析提出一种新的方法,将相似性视为关系和属性.
- 开发一个计算管道和视觉分析工具,用于探索大型数据集,特别是科学出版物.
- 为了使用户能够调查变化的相似性标准对数据探索的影响.
主要方法:
- 开发了一个基于嵌入的计算管道来模拟相似性.
- 创建了一种原型视觉分析工具,用于对科学出版物进行交互式探索.
- 将相似性视为双重概念:物品之间的关系和具有自身分布的独特属性.
主要成果:
- 拟议的方法允许更全面地了解基于相似性的数据结构.
- 视觉分析工具促进了动态探索,使用户能够评估不同相似性标准的影响.
- 通过两种不同的用例证明了该方法的实用性,涉及科学出版物.
结论:
- 这种新的方法扩大了基于相似性的分析的潜力,超越了传统的聚类和维度减少.
- 开发的管道和工具提供了一个强大的手段,用于在大型科学文献数据集中的理解.
- 该方法提供了关于相似性标准如何塑造数据关系和模式的见解.
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