Data scientists across disciplines are increasingly in need of exploratory analysis tools for data sets with a high volume of features. We expand upon graph mining approaches for exploratory analysis of high-dimensional data to introduce Sirius, a visualization package for researchers to explore feature relationships among mixed data types using mutual information and network backbone sparsification. Visualizations of feature relationships aid data scientists in finding meaningful dependence among features, which can engender further analysis for feature selection, feature extraction, projection, identification of proxy variables, or insight into temporal variation at the macro scale. Graph mining approaches for feature analysis exist, such as association networks of binary features, or correlation networks of quantitative features, but mixed data types present a unique challenge for developing comprehensive feature networks for exploratory analysis. Using an information theoretic approach, Sirius supports heterogeneous data sets consisting of binary, continuous quantitative, and discrete categorical data types, and provides a user interface exploring feature pairs with high mutual information scores. We leverage a backbone sparsification approach from network theory as a dimensionality reduction technique, which probabilistically trims edges according to the local network context. Sirius is an open source Python package and Django web application for exploratory visualization, which can be deployed in data analysis pipelines. The Sirius codebase and exemplary data sets can be found at \url{https://github.com/compstorylab/sirius}.