Huong N. Higgins

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The ultimate goal of any visual analytic task is to make sense of the data and gain insights. Unfortunately, the continuously growing scale of the data nowadays challenges the traditional data analytics in the ”big-data” era. Particularly, the human cognitive capabilities are constant whereas the data scale is not. Furthermore, most existing work focus on(More)
Linear models are commonly used to identify trends in data. While it is an easy task to build linear models using pre-selected variables, it is challenging to select the best variables from a large number of alternatives. Most metrics for selecting variables are global in nature, and thus not useful for identifying local patterns. In this work, we present(More)
A significant task within data mining is to identify data models of interest. While facilitating the exploration tasks, most visualization systems do not make use of all the data models that are generated during the exploration. In this paper, we introduce a system that allows the user to gain insights from the data space progressively by forming data(More)
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