  The availability of large data sets is providing an impetus for driving
current artificial intelligent developments. There are, however, challenges for
developing solutions with small data sets due to practical and cost-effective
deployment and the opacity of deep learning models. The Comprehensive
Abstraction and Classification Tool for Uncovering Structures called CACTUS is
presented for improved secure analytics by effectively employing explainable
artificial intelligence. It provides additional support for categorical
attributes, preserving their original meaning, optimising memory usage, and
speeding up the computation through parallelisation. It shows to the user the
frequency of the attributes in each class and ranks them by their
discriminative power. Its performance is assessed by application to the
Wisconsin diagnostic breast cancer and Thyroid0387 data sets.
