Visual Analytics and Imaging Laboratory (VAI Lab)
Computer Science Department, Stony Brook University, NY
Abstract: Numerous methods have been described that allow the visualization of the data matrix. But all suffer from a common problem – observing the data points in the context of the attributes is either impossible or inaccurate. We describe a method that allows these types of comprehensive layouts. We achieve it by combining two similarity matrices typically used in isolation – the matrix encoding the similarity of the attributes and the matrix encoding the similarity of the data points. This combined matrix yields two of the four submatrices needed for a full multi-dimensional scaling type layout. The remaining two submatrices are obtained by creating a fused similarity matrix – one that measures the similarity of the data points with respect to the attributes, and vice versa. The resulting layout places the data objects in direct context of the attributes and hence we call it the data context map. It allows users to simultaneously appreciate (1) the similarity of data objects, (2) the similarity of attributes in the specific scope of the collection of data objects, and (3) the relationships of data objects with attributes and vice versa. The contextual layout also allows data regions to be segmented and labeled based on the locations of the attributes. This enables, for example, the map’s application in selection tasks where users seek to identify one or more data objects that best fit a certain configuration of factors, using the map to visually balance the tradeoffs.
Teaser: An actual use case: finding the university that best fits a user's personal critera
The process of finding the university that best fits a user's personal critera. (a) The purple regions has universities with good academics (college score > 9). (b) The orange region has good athletics (college score >9). (c) Combining regions (a) and (b). (d) The green region has universities with low tuition (<$18,000). (e) Combining regions (c) and (d) -- there really is no university that fits all of these criteria and therefore some tradeoffs are needed. School A has low tuition and good athletics, but academics is lacking. School B has good academics and atheletics, but it is more expensive. School C has good academics and low tuition, but low athletics activities. Visualizing the competing universities in the context of all selection criteria in a single display makes it easy to weigh the tradeoffs of this important decision.
Video: Watch it to get a quick overview:
Paper: S. Cheng, K. Mueller, "The Data Context Map: Fusing Data and Attributes into a Unified Display," IEEE Trans. on Visualization and Computer Graphics, 22(1): 121-130, 2016. ppt pdf