交互模式的视觉提取以层次聚类和过程采矿为指导
IEEE transactions on visualization and computer graphics
|November 20, 2025
概括
本研究提出了一种用于分析大量用户交互数据的视觉分析方法. 它有助于发现非结构化交互序列中的模式,使用集群和过程挖掘来提高系统可用性.
科学领域:
- 人与计算机的交互
- 数据可视化 数据可视化
- 数据挖掘 数据挖掘
背景情况:
- 分析用户行为和提高系统可用性取决于理解用户交互.
- 大量,非结构化的交互序列数据在模式发现方面带来了挑战.
研究的目的:
- 引入一种视觉分析方法来探索大型,非结构化的交互序列数据.
- 支持分析师发现有意义的交互模式.
主要方法:
- 集成的等级集群和过程挖掘技术.
- 采用基于动态时间扭曲的相似度测量方法进行序列比较.
- 提供逐步,交互导航的集群结果与视觉线索.
主要成果:
- 成功使分析师能够探索大型交互序列数据.
- 促进了逐渐发现有意义的交互模式.
- 通过案例研究证明了有效性和适用性.
结论:
- 视觉分析方法有效支持对用户交互数据的分析.
- 集群和过程挖掘的整合有助于模式发现和系统可用性改进.
- 该系统适用于系统设计师,开发人员和领域专家.
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