  Recently, the field of machine learning has undergone a transition from
model-centric to data-centric. The advancements in diverse learning tasks have
been propelled by the accumulation of more extensive datasets, subsequently
facilitating the training of larger models on these datasets. However, these
datasets remain relatively under-explored. To this end, we introduce a
pioneering approach known as RK-core, to empower gaining a deeper understanding
of the intricate hierarchical structure within datasets. Across several
benchmark datasets, we find that samples with low coreness values appear less
representative of their respective categories, and conversely, those with high
coreness values exhibit greater representativeness. Correspondingly, samples
with high coreness values make a more substantial contribution to the
performance in comparison to those with low coreness values. Building upon
this, we further employ RK-core to analyze the hierarchical structure of
samples with different coreset selection methods. Remarkably, we find that a
high-quality coreset should exhibit hierarchical diversity instead of solely
opting for representative samples. The code is available at
https://github.com/yaolu-zjut/Kcore.
