Abstract This article addresses the issue of assigning items to different test dimensions (e.g., determining which dimension an item belongs to) with cluster analysis. Previously, hierarchical methods have been used (Roussos et al. 1997); however, the findings here suggest that an iterative reallocation partitioning (IRP) algorithm provides interpretively similar solutions and statistically better solutions to the problem. More importantly, it is shown that the inherent nature of locally optimal solutions in the IRP algorithm leads to a method that aids in determining the appropriateness of performing a cluster analysis—a feature that is lacking in the standard hierarchical methods currently in the literature.

中文翻译：

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使用迭代重新分配分区算法验证测试多维性

摘要 本文解决了使用聚类分析将项目分配到不同测试维度（例如，确定项目属于哪个维度）的问题。以前，已经使用了分层方法（Roussos 等人，1997 年）；然而，这里的研究结果表明，迭代重新分配分区 (IRP) 算法为问题提供了解释上相似的解决方案和统计上更好的解决方案。更重要的是，它表明 IRP 算法中局部最优解的固有性质导致了一种有助于确定执行聚类分析的适当性的方法——这是目前文献中的标准分层方法所缺乏的特征。