  Motivation: Real-world data often contain measurements with both continuous
and discrete values. Despite the availability of many libraries, data sets with
mixed data types require intensive pre-processing steps, and it remains a
challenge to describe the relationships between variables. The data
understanding phase is an important step in the data mining process, however,
without making any assumptions on the data, the search space is
super-exponential in the number of variables. Methods: We propose graphical
hypergeometric networks (HNet), a method to test associations across variables
for significance using statistical inference. The aim is to determine a network
using only the significant associations in order to shed light on the complex
relationships across variables. HNet processes raw unstructured data sets and
outputs a network that consists of (partially) directed or undirected edges
between the nodes (i.e., variables). To evaluate the accuracy of HNet, we used
well known data sets and in addition generated data sets with known ground
truth. The performance of HNet is compared to Bayesian structure learning.
Results: We demonstrate that HNet showed high accuracy and performance in the
detection of node links. In the case of the Alarm data set we can demonstrate
on average an MCC score of 0.33 + 0.0002 (P<1x10-6), whereas Bayesian structure
learning resulted in an average MCC score of 0.52 + 0.006 (P<1x10-11), and
randomly assigning edges resulted in a MCC score of 0.004 + 0.0003 (P=0.49).
Conclusions: HNet can process raw unstructured data sets, allows analysis of
mixed data types, it easily scales up in number of variables, and allows
detailed examination of the detected associations. Availability:
https://erdogant.github.io/hnet/
