Leveraging layout with dimensional stacking and pixelization to facilitate feature discovery and directed queries

Authors:
John T. Langton;Astrid A. Prinz;David K. Wittenberg;Timothy J. Hickey
Affiliations:
Charles River Analytics Inc.;Department of Biology, Emory University;Computer Science Department, Brandeis University;Computer Science Department, Brandeis University
Venue:
VIEW'06 Proceedings of the 1st first visual information expert conference on Pixelization paradigm
Year:
2006

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Abstract

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 feature discovery, informs and directs user queries, supports interactive data mining, and provides a means for exploratory analysis. In this paper we describe our approach and how it is being used to analyze multidimensional, multivariate neuroscience data.