New IEEE TVCG Article on Rare Category Visualization
IEEE Transactions on Visualization and Computer Graphics (TVCG) just published a paper I wrote collaboratively with a number of colleagues on Rare Category Visualization. The paper is now available online and will appear eventually in a print issue of the journal. Here is the abstract from the paper:
Rare category identification is an important task in many application domains, ranging from network security, to financial fraud detection, to personalized medicine. These are all applications which require the discovery and characterization of sets of rare but structurally-similar data entities which are obscured within a larger but structurally different dataset. This paper introduces RCLens, a visual analytics system designed to support user-guided rare category exploration and identification. RCLens adopts a novel active learning-based algorithm to iteratively identify more accurate rare categories in response to user-provided feedback. The algorithm is tightly integrated with an interactive visualization-based interface which supports a novel and effective workflow for rare category identification. This paper (1) defines RCLens’ underlying active-learning algorithm; (2) describes the visualization and interaction designs, including a discussion of how the designs support user-guided rare category identification; and (3) presents results from an evaluation demonstrating RCLens’ ability to support the rare category identification process.
Hanfei Lin, Siyuan Gao, David Gotz, Fan Du, Jingrui He, and Nan Cao. RCLens: Interactive Rare Category Exploration and Identification. IEEE Transactions on Visualization and Computer Graphics (Published online on June 6, 2017. To appear in print.).
[This article is available through the journal’s website. View the article by clicking here.]