对问卷答复的基于因果关系的视觉分析
IEEE transactions on visualization and computer graphics
|October 30, 2023
概括
本研究引入了在问卷分析中对因果推理的新系统解决方案,使其更有效. 它有助于分析师有效地探索数据并推导因果关系,克服现有方法的局限性.
科学领域:
- 数据科学数据科学数据科学
- 因果推理因果推理
- 调查 调查 分析 分析
背景情况:
- 因果推理对于从问卷数据中提取可操作的见解至关重要.
- 经典的统计方法和现有的视觉工具在复杂的问卷数据的可扩展性和专家知识整合方面面临挑战.
- 目前在问卷中发现因果关系往往是耗时的,并且依赖于试错.
研究的目的:
- 开发一个系统和有效的解决方案,用于因果推理在调查问卷分析.
- 为了能够有效地探索问卷数据和推导因果关系.
- 解决现有方法在处理大搜索空间和复杂的因果结构方面的局限性.
主要方法:
- 利用关联挖掘算法来识别问题组合与潜在因果关系.
- 开发了一种交互式方法来探索因果子图.
- 将来自专家的需求集成到一个可视化工具中.
主要成果:
- 拟议的系统有助于有效地探索调查问卷数据以进行因果推断.
- 一项比较研究证明了开发的可视化工具的可用性和效率与最先进的系统相比.
- 关联挖矿方法有效地揭示了问题之间的潜在因果关系.
结论:
- 提出的系统解决方案提高了在问卷分析中的因果推理的有效性和效率.
- 通过专家输入的可视化工具,改善了因果关系的交互式探索.
- 这项工作提供了一种可扩展和实用的方法,用于从复杂的调查数据中推导因果关系.
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