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Chania

Towards Understanding Human Similarity Perception in the Analysis of Large Sets of Scatter Plots

Chania

Searching for big insights on online reviews

Chania

NYU Vis Lab won top honors in two challenges organized by United Nations

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  • IEEE VIS 2016

    TextTile: An Interactive Visualization Tool for Seamless Exploratory Analysis of Structured Data and Unstructured Text

    TextTile is a data visualization tool for investigation of datasets and questions that require seamless and flexible analysis of structured data and unstructured text.
  • CHI 2016

    Towards Understanding Human Similarity Perception in the Analysis of Large Sets of Scatter Plots

    In this paper, we present a user study aimed at understanding how human observers judge scatter plot similarity when presented with a large set of iconic scatter plot representations. The study intends to advance the state of the art of the use of quality measures as an exploration and discovery tool in information visualization.
  • CHI 2015

    How deceptive are deceptive visualizations?: An empirical analysis of common distortion techniques

    In this paper, we present an empirical analysis of deceptive visualizations. We start with an in-depth analysis of what deception means in the context of data visualization, and categorize deceptive visualizations based on the type of deception they lead to. We identify popular distortion techniques and the type of visualizations those distortions can be applied to, and formalize why deception occurs with those distortions.
  • IEEE VIS 2014

    INFUSE - Interactive Feature Selection for Predictive Modeling of High Dimensional Data

    INFUSE is a novel visual analytics system designed to help analysts understand how predictive features are being ranked across feature selection algorithms, cross-validation folds, and classifiers.
  • March 11, 2016
    Searching for big insights on online reviews
  • November 23, 2015
    NYU Vis Lab won top honors in two challenges organized by United Nations
  • NYU VisLab
  • enrico.bertini@nyu.edu
  • nyuvis
  • FILWD