VennSOM: A SOM-Assisted Visualization of Binary Data

Abstract

Venn diagrams are a useful method of visualizing Boolean data; however, their data aggregation causes fine detail about the data to be lost. In this paper, we present a method of augmenting Venn diagrams, so that they may depict similarity relationships among individual records in the data using the Self-Organizing Map. We applied this method to a synthetic data set and an empirical proteomics data set. We found that we were able to separate data within each region of the Venn diagram based on dimensional values, and that we can highlight the clustering of $p$-values in the empirical set.