Dimensionality is an important aspect for analyzing and understanding (high-dimensional) data. In their 2006 ICDM paper Tatti et al. answered the question for a (interpretable) dimension of binary data tables by introducing a normalized correlation dimension. In the present work we revisit their results and contrast them with a concept based notion of intrinsic dimension (ID) recently introduced for geometric data sets. To do this, we present a novel approximation for this ID that is based on computing concepts only up to a certain support value. We demonstrate and evaluate our approximation using all available datasets from Tatti et al., which have between 469 and 41271 extrinsic dimensions.

翻译：维度是分析和理解（高维）数据的重要方面。在2006年ICDM论文中，Tatti等人通过引入归一化相关维度，回答了（可解释的）二进制数据表的维度问题。本文重新审视了他们的结果，并与近期针对几何数据集提出的基于概念的固有维度（ID）概念进行对比。为此，我们提出了一种新的近似方法，该方法仅基于支持度阈值以上的概念进行ID计算。我们使用Tatti等人提供的所有数据集（外部分维数介于469至41271之间）对所提出的近似方法进行了验证与评估。