  The Mapper algorithm is a visualization technique in topological data
analysis (TDA) that outputs a graph reflecting the structure of a given
dataset. However, the Mapper algorithm requires tuning several parameters in
order to generate a ``nice" Mapper graph. This paper focuses on selecting the
cover parameter. We present an algorithm that optimizes the cover of a Mapper
graph by splitting a cover repeatedly according to a statistical test for
normality. Our algorithm is based on $G$-means clustering which searches for
the optimal number of clusters in $k$-means by iteratively applying the
Anderson-Darling test. Our splitting procedure employs a Gaussian mixture model
to carefully choose the cover according to the distribution of the given data.
Experiments for synthetic and real-world datasets demonstrate that our
algorithm generates covers so that the Mapper graphs retain the essence of the
datasets, while also running significantly fast.
