Leveraging Layout with Dimensional Stacking and Pixelization to Facilitate Feature Discovery and Directed Queries

Pixelization is the simple yet powerful technique of mapping each element of some data set to a pixel in a 2D image. There are 2 primary characteristics of pixels that can be leveraged to impart information: 1. their color and color-related attributes (hue, saturation, etc.) and 2. their arrangement in the image. We have found that applying a dimensional stacking layout to pixelization uniquely facilitates interactive data mining, directs user queries, and provides a type of visual principle components analysis. In this paper we describe our approach and how it is being used to analyze multidimensional, multivariate neuroscience data.