A correlative analysis process in a visual analytics environment

Malik, Abish; Maciejewski, Ross; Elmqvist, Niklas; Jang, Yun; Ebert, David S.; Huang, Whitney K.

doi:10.1109/vast.2012.6400491

Cited by 44 publications

(23 citation statements)

References 25 publications

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“…Berger et al [14] introduce an interactive approach to inspect the parameter space in comparison to multiple target values. Malik et al [15] describe a framework that facilitates the interactive execution of auto-correlation methods. Steed et al [16] describe a visual analytics methodology where statistical regression, correlation analysis, and descriptive statistical calculations are incorporated as visual guidance within an interactive system that enables the identification of important variables that can act as significant predictors.…”

Section: Related Workmentioning

confidence: 99%

Supporting theoretically-grounded model building in the social sciences through interactive visualisation

et al. 2017

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractThe primary purpose for which statistical models are employed in the social sciences is to understand and explain phenomena occurring in the world around us. In order to be scientifically valid and actionable, the construction of such models need to be strongly informed by theory. To accomplish this, there is a need for methodologies that can enable scientists to utilise their domain knowledge effectively even in the absence of strong a priori hypotheses or whilst dealing with complex datasets containing hundreds of variables and leading to large numbers of potential models. In this paper, we describe enhanced model building processes in which we use interactive visualisations as the underlying mechanism to facilitate the construction and documentation of theory-driven models. We report our observations from a collaborative project involving social and computer scientists, and identify key roles for visualisation to support model building within the context of social science. We describe a suite of techniques to facilitate the exploration of statistical summaries of input variables, to compare the quality of alternative models, and to keep track of the model-building process. We demonstrate how these techniques operate in coordination to allow social scientists to efficiently generate models that are tightly underpinned by domain specific theory.

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Section: Related Workmentioning

confidence: 99%

Supporting theoretically-grounded model building in the social sciences through interactive visualisation

et al. 2017

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“…[51], [52], [53], [54], [55], [56] Groups & Classification [57] [58], [59] [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74] [75], [76], [77], [78], [79], [80] Dependence & Prediction [81], [82], [46] [83], [84], [85], [86], [87], [88], [89] [90], [91], [92] being analyzed. The results are then presented to the user through different visual encodings that are often accompanied by interaction.…”

Section: Levels Of Integrationmentioning

confidence: 99%

“…Malik et al [91] describe a framework for interactive auto-correlation. This is an example where the correlation analysis is tightly coupled with the interactive elements in the visualization solution.…”

Section: Tight Integrationmentioning

confidence: 99%

On Computationally-Enhanced Visual Analysis of Heterogeneous Data and Its Application in Biomedical Informatics

Turkay

Jeanquartier

Holzinger

et al. 2014

Lecture Notes in Computer Science

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“…Many recent papers, especially in the Visual Analytics community, focus on different approaches to combine visualization and automated data analysis (Malik et al 2012;Hoferlin et al 2012;Brown et al 2012;Endert et al 2011;Jeong et al 2009), especially in the context of supervised machine learning (Choo et al 2010;Heimerl et al 2012). Only few attempts have been made to visualize decision boundaries for multi-dimensional data (Poulet 2008;Caragea et al 2001;Hamel 2006).…”

Section: Related Workmentioning

confidence: 99%

Visualizing multi-dimensional decision boundaries in 2D

Migut

Worring

Veenman³

2013

Data Min Knowl Disc

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In many applications experts need to make decisions based on the analysis of multi-dimensional data. Various classification models can support the decision making process. To obtain an intuitive understanding of the classification model, interactive visualizations are essential. We argue that this is best done by a series of interactive 2D scatterplots. In this paper, we define a set of characteristics of the multi-dimensional classification model that have to be visually represented in those scatterplots. Our proposed method presents those characteristics in a uniform manner for both linear and non-linear classification methods. We combine a visualization of a Voronoi based representation of multi-dimensional decision boundaries with visualization of the distances of the data elements to these boundaries. To allow the developer of the model to refine the threshold of the classification model and instantly observe the results, we use interactive decision point selection on a performance curve. Finally, we show how the combination of those techniques allows exploration of multi-dimensional decision boundaries in 2D.

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A correlative analysis process in a visual analytics environment

Cited by 44 publications

References 25 publications

Supporting theoretically-grounded model building in the social sciences through interactive visualisation

Supporting theoretically-grounded model building in the social sciences through interactive visualisation

On Computationally-Enhanced Visual Analysis of Heterogeneous Data and Its Application in Biomedical Informatics

Visualizing multi-dimensional decision boundaries in 2D

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