RCLens: Interactive Rare Category Exploration and Identification

Hanfei Lin, Siyuan Gao, David Gotz, Fan Du, Jingrui He, Nan Cao

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)2223-2237
Number of pages15
JournalIEEE Transactions on Visualization and Computer Graphics
Issue number7
StatePublished - Jul 1 2018
Externally publishedYes


  • information visualization
  • machine learning
  • rare category detection
  • Visual analytics

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design


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