By providing a complete record of time use for a given population, time use studies enable investigators to test various hypotheses concerning that behavior. However, the large number and variety of activity combinations that are relevant in time allocation choices and, therefore, time use analysis, makes measuring or even fully identifying all of them impossible without the proper data mining tools. In this paper, we propose a framework for mining sequences of activities to capture more complex patterns than those currently available on how individuals organize their days. The proposed framework was applied to the American Time Use Surveys (ATUS) dataset to explore individual time allocation behavior, identifying sequences of activities that are frequent. For example, patterns such as the preferred activities that are performed before and after specific activities (such as paid work or leisure) are discussed in terms of their frequency. Such patterns are not easy to reveal using traditional descriptive analysis.

中文翻译：

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挖掘活动中的序列以进行时间使用分析

通过提供给定人群的时间使用情况的完整记录，时间使用情况研究使研究人员能够测试有关该行为的各种假设。但是，与时间分配选择相关的大量活动组合以及因此而进行的时间使用分析使得在没有合适的数据挖掘工具的情况下无法测量甚至完全识别所有活动。在本文中，我们提出了一个框架，用于挖掘活动序列，以捕获比当前个人如何组织日程更为复杂的模式。拟议的框架已应用于美国时间使用调查（ATUS）数据集，以探索个人的时间分配行为，确定频繁的活动序列。例如，根据特定频率（例如有偿工作或休闲）之前和之后执行的首选活动等模式进行讨论。使用传统的描述性分析很难发现这种模式。