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在序列集群中识别和鉴定偏差病例:为什么和如何

Raffaella Piccarreta¹, Emanuela Struffolino²

¹Department of Decision Sciences, "Carlo F. Dondena" Centre for Research on Social Dynamics, BIDSA (Bocconi Institute for Data Science and Analytics), Bocconi University, via Guglielmo Röntgen 1, 20136, Milan, Italy.

European journal of population = Revue europeenne de demographie

|December 19, 2023

概括

本研究介绍了在序列分析中识别典型和不寻常模式的方法,从而提高了数据的理解. 这些工具有助于检测异常值,并增强集群分析,以获得对纵向过程的更详细见解.

科学领域:

社会科学社会科学社会科学
人口统计学人口统计学
社会学社会学社会学

背景情况:

序列分析在社会科学中用于理解以状态序列表示的纵向过程.
聚类序列识别出典型的模式,但异常值可能会影响均性和解释.

研究的目的:

制定评估集群组成和检测偏差序列的标准.
提供可视化和区分常规和偏差病例的工具.
为了提高序列数据分析的准确性和细分性.

主要方法:

探索检查内部集群组成的标准.
开发用于检测异常序列 (罕见模式或异常值) 的方法.
引入可视化工具来区分正规和偏差病例.

主要成果:

提出的方法允许对序列数据结构进行更准确,更细致的描述.
识别典型的序列类型和特殊的,潜在的重要异常序列.
在生命周期社会人口统计学中的实用性证明,分析意大利妇女的就业轨迹.

结论:

开发的方法改善了典型和偏差序列的识别,提供了更深入的见解.
这些工具对于假设集群同质性的应用非常有价值,特别是在回归分析中.
这些方法通过突出独特的案例来增强对时间过程的实质和理论理解.

关键词:

集群分析就是对集群进行分析.标记的指数图表标记的指数图表索引图表索引图表索引图表序列分析是指进行序列分析.视觉化视觉化视觉化

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