日常生活发现:挖掘和可视化社会科学日记数据中的活动模式

K. Vrotsou, K. Ellegård, M. Cooper
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引用次数: 46

摘要

识别和检查活动模式的能力是社会和行为科学的关键工具。在过去,这是通过统计或纯粹的可视化方法完成的,但通过复杂的数据挖掘和可视化工具进行模式定位和评估的自动化顺序模式分析可以为数据的交互式探索开辟新的可能性。本文描述了在视觉活动分析工具visual - timepacts中增加一种序列模式识别方法,以及它在社会科学日记数据模式分析过程中的有效性。结果表明,该方法正确地识别模式,并有效地将其传达给社会科学家,使他们能够快速而容易地理解模式的意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Everyday Life Discoveries: Mining and Visualizing Activity Patterns in Social Science Diary Data
The ability to identify and examine patterns of activities is a key tool for social and behavioural science. In the past this has been done by statistical or purely visual methods but automated sequential pattern analysis through sophisticated data mining and visualization tools for pattern location and evaluation can open up new possibilities for interactive exploration of the data. This paper describes the addition of a sequential pattern identification method to the visual activity-analysis tool, VISUAL-TimePAcTS, and its effectiveness in the process of pattern analysis in social science diary data. The results have shown that the method correctly identifies patterns and conveys them effectively to the social scientist in a manner that allows them quick and easy understanding of the significance of the patterns.
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