Castor, Pollux, and Gemini are three different implementations of a computational pipeline that projects high-dimensional data into an interactive visualization, combining both dimension reduction and clustering algorithms in the projection. In addition to DR and clustering, semantic interactions are implemented within the interface, allowing an analyst to provide feedback to an underlying learning component. With this feedback, an analyst can interact directly with the data to express their interests, incrementally training those underlying models and causing future updates to the visualization to better reflect the dimensions of interest to the analyst.
The overarching goal of this research is to explore both the design and interaction space at the intersection of dimension reduction algorithms, clustering algorithms, and semantic interaction.
- John Wenskovitch and Chris North. “Pollux: Interactive Cluster-First Projections of High-Dimensional Data,” in 2019 Symposium on Visualization in Data Science. VDS’19. Vancouver, BC, Canada, 2019. Acceptance Rate: 29.6%.
- John Wenskovitch, Ian Crandell, Naren Ramakrishnan, Leanna House, Scotland Leman, and Chris North. “Towards a Systematic Combination of Dimension Reduction and Clustering in Visual Analytics,” IEEE Transactions on Visualization and Computer Graphics, 24(1) (Jan. 2018), pp. 131–141. DOI: 10.1109/TVCG.2017.2745258. Presented at IEEE VIS (VAST) 2017; Acceptance Rate: 21.4%. Journal Impact Factor: 2.84.
- John Wenskovitch and Chris North. “Observation-Level Interaction with Clustering and Dimension Reduction Algorithms,” in Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics. HILDA’17. Chicago, IL, USA: ACM, 2017, 14:1–14:6. DOI: 10.1145/3077257.3077259.