Article

# Variable binned scatter plots.

Information Visualization (Impact Factor: 0.77). 01/2010; 9:194-203. DOI: 10.1057/ivs.2010.4

Source: DBLP

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**ABSTRACT:**We introduce Splatterplots, a novel presentation of scattered data that enables visualizations that scale beyond standard scatter plots. Traditional scatter plots suffer from overdraw (overlapping glyphs) as the number of points per unit area increases. Overdraw obscures outliers, hides data distributions, and makes the relationship among subgroups of the data difficult to discern. To address these issues, Splatterplots abstract away information such that the density of data shown in any unit of screen space is bounded, while allowing continuous zoom to reveal abstracted details. Abstraction automatically groups dense data points into contours and samples remaining points. We combine techniques for abstraction with perceptually based color blending to reveal the relationship between data subgroups. The resulting visualizations represent the dense regions of each subgroup of the data set as smooth closed shapes and show representative outliers explicitly. We present techniques that leverage the GPU for Splatterplot computation and rendering, enabling interaction with massive data sets. We show how Splatterplots can be an effective alternative to traditional methods of displaying scatter data communicating data trends, outliers, and data set relationships much like traditional scatter plots, but scaling to data sets of higher density and up to millions of points on the screen.IEEE transactions on visualization and computer graphics. 09/2013; 19(9):1526-1538. -
##### Article: Visual-interactive Exploration of Interesting Multivariate Relations in Mixed Research Data Sets

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**ABSTRACT:**The analysis of research data plays a key role in data-driven areas of science. Varieties of mixed research data sets exist and scientists aim to derive or validate hypotheses to find undiscovered knowledge. Many analysis techniques identify relations of an entire dataset only. This may level the characteristic behavior of different subgroups in the data. Like automatic subspace clustering, we aim at identifying interesting subgroups and attribute sets. We present a visual-interactive system that supports scientists to explore interesting relations between aggregated bins of multivariate attributes in mixed data sets. The abstraction of data to bins enables the application of statistical dependency tests as the measure of interestingness. An overview matrix view shows all attributes, ranked with respect to the interestingness of bins. Complementary, a node-link view reveals multivariate bin relations by positioning dependent bins close to each other. The system supports information drill-down based on both expert knowledge and algorithmic support. Finally, visual-interactive subset clustering assigns multivariate bin relations to groups. A list-based cluster result representation enables the scientist to communicate multivariate findings at a glance. We demonstrate the applicability of the system with two case studies from the earth observation domain and the prostate cancer research domain. In both cases, the system enabled us to identify the most interesting multivariate bin relations, to validate already published results, and, moreover, to discover unexpected relations.Computer Graphics Forum 06/2014; 33(3). · 1.60 Impact Factor - [Show abstract] [Hide abstract]

**ABSTRACT:**Regression models play a key role in many application domains for analyzing or predicting a quantitative dependent variable based on one or more independent variables. Automated approaches for building regression models are typically limited with respect to incorporating domain knowledge in the process of selecting input variables (also known as feature subset selection). Other limitations include the identification of local structures, transformations, and interactions between variables. The contribution of this paper is a framework for building regression models addressing these limitations. The framework combines a qualitative analysis of relationship structures by visualization and a quantification of relevance for ranking any number of features and pairs of features which may be categorical or continuous. A central aspect is the local approximation of the conditional target distribution by partitioning 1D and 2D feature domains into disjoint regions. This enables a visual investigation of local patterns and largely avoids structural assumptions for the quantitative ranking. We describe how the framework supports different tasks in model building (e.g., validation and comparison), and we present an interactive workflow for feature subset selection. A real-world case study illustrates the step-wise identification of a five-dimensional model for natural gas consumption. We also report feedback from domain experts after two months of deployment in the energy sector, indicating a significant effort reduction for building and improving regression models.IEEE transactions on visualization and computer graphics. 12/2013; 19(12):1962-71.

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