Regression cube: A technique for multidimensional visual exploration and interactive pattern finding

Yu Hsuan Chan, Carlos D. Correa, Kwan-Liu Ma

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

Scatterplots are commonly used to visualize multidimensional data; however, 2D projections of data offer limited understanding of the high-dimensional interactions between data points.We introduce an interactive 3D extension of scatterplots called the Regression Cube (RC), which augments a 3D scatterplot with three facets on which the correlations between the two variables are revealed by sensitivity lines and sensitivity streamlines. The sensitivity visualization of local regression on the 2D projections provides insights about the shape of the data through its orientation and continuity cues. We also introduce a series of visual operations such as clustering, brushing, and selection supported in RC. By iteratively refining the selection of data points of interest, RC is able to reveal salient local correlation patterns that may otherwise remain hidden with a global analysis.We have demonstrated our system with two examples and a user-oriented evaluation, and we show how RCs enable interactive visual exploration of multidimensional datasets via a variety of classification and information retrieval tasks. A video demo of RC is available.

Original languageEnglish (US)
Article number7
JournalACM Transactions on Interactive Intelligent Systems
Volume4
Issue number1
DOIs
StatePublished - Jan 1 2014

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Artificial Intelligence

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