  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.
