A Constrained Randomization Approach to Interactive Visual Data Exploration with Subjective Feedback

Abstract

Data visualization and iterative/interactive data mining are growing rapidly in attention, both in research as well as in industry. However, while there are plethora of advanced data mining methods and lots of works in the field of visualisation, integrated methods that combine advanced visualization and/or interaction with data mining techniques in a principled way are rare. We present a framework based on constrained randomization which lets users explore high-dimensional data via 'subjectively informative' two-dimensional data visualizations. The user is presented with 'interesting' projections, allowing users to express their observations using visual interactions that update a background model representing the user's belief state. This background model is then considered by a projection-finding algorithm employing data randomization to compute a new 'interesting' projection. By providing users with information that contrasts with the background model, we maximize the chance that the user encounters striking new information present in the data. This process can be iterated until the user runs out of time or until the difference between the randomized and the real data is insignificant. We present two case studies, one controlled study on synthetic data and another on census data, using the proof-of-concept tool SIDE that demonstrates the presented framework.

Model selection with bootstrap validation

“…One such generic scenario is interactive data analysis. A typical workflow in such a setup is a user (or algorithm) that sees a view in the data, based on which they proceed to the next step, obtaining another view, and so forth; see, for example, [15,28,29]. During the exploration, the user can observe various patterns in the data.…”