Scatterplot Matrix Techniques for Large N

Carr, Daniel B.; Littlefield, Richard J.; Nichloson, Wesley L.

doi:10.2307/2289444

Cited by 195 publications

(160 citation statements)

References 11 publications

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“…This implies that we could handle a much larger number of data points than the maximum number used in this study (35,620). We employed the three-channel RGB colour format with 8 bits for each channel.…”

Section: Gku As An Outlier Detection Methodsmentioning

confidence: 99%

“…The variable binned scatter plots technique allows visualization of large amounts of data without overlaps [30]. This technique incorporates variable size bins [35] and classifies them into different groups using a colour scheme. The Splatter-plots technique automatically groups dense data points into contours and samples the remaining points.…”

Section: Introductionmentioning

confidence: 99%

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Continuous Learning Graphical Knowledge Unit for Cluster Identification in High Density Data Sets

et al. 2016

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Big data are visually cluttered by overlapping data points. Rather than removing, reducing or reformulating overlap, we propose a simple, effective and powerful technique for density cluster generation and visualization, where point marker (graphical symbol of a data point) overlap is exploited in an additive fashion in order to obtain bitmap data summaries in which clusters can be identified visually, aided by automatically generated contour lines. In the proposed method, the plotting area is a bitmap and the marker is a shape of more than one pixel. As the markers overlap, the red, green and blue (RGB) colour values of pixels in the shared region are added. Thus, a pixel of a 24-bit RGB bitmap can code up to 2 24 (over 1.6 million) overlaps. A higher number of overlaps at the same location makes the colour of this area identical, which can be identified by the naked eye. A bitmap is a matrix of colour values that can be represented as integers. The proposed method updates this matrix while adding new points. Thus, this matrix can be considered as an up-to-time knowledge unit of processed data. Results show cluster generation, cluster identification, missing and out-of-range data visualization, and outlier detection capability of the newly proposed method.

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Section: Gku As An Outlier Detection Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Continuous Learning Graphical Knowledge Unit for Cluster Identification in High Density Data Sets

et al. 2016

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“…Much of the original work was done on scatter plot matrices (Becker and Cleveland, 1987;Carr et al, 1987). Brushing is used in many multiple-view systems from multi-variate matrix plots, coplot matrices (Brunsdon, 2001) to other geographic exploratory analysis (Monmonier, 1989).…”

Section: Current Themes In Exploratory (Multiple View) Visualizationmentioning

confidence: 99%

Exploratory Visualization with Multiple Linked Views

Roberts

2005

Exploring Geovisualization

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Exploratory visualization enables the user to test scenarios and investigate possibilities. Through an exploration, the user may change various parameter values of a visualization system that in turn alters the appearance of the visual result. For example, the changes made may update what information is being displayed, the quantity or resolution of the information, the type of the display (say) from scatter plot to line-graph. Furthermore, the user may generate additional windows that contain the visual result of the new parameters so they can compare different ideas side-by-side (these multiple views may persist such that the user can compare previous incarnations). Commonly these windows are linked together to allow further investigation and discovery, such as selection by brushing or combined navigation. There are many challenges, such as linking multiple views with different data, initializing the different views, indicating to the user how the different views are linked. This chapter provides a review of current multiple linked-view tools, methodologies and models, discusses related challenges and ideas, and provides some rudiments for coordination within a geovisualization context. The types and uses of coordination for exploratory visualization are varied and diverse, these ideas are underused in geovisualization and exploratory visualization in general. Thus, further research needs to occur to develop specific geovisualization reference models and extensible systems that incorporate the rich variety of possible coordination exploration ideas. IntroductionThis chapter advocates the use of many lightweight views that are linked together. They are lightweight in that they are: (i) easy to generate by the user, where the user does not spend unnecessary time and effort to explicitly link the new view to existing ones; and (ii) do not take many computer resources (e.g., memory, computation). Such multiple linked

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“…Bivariate histograms and back-to-back stem-and-leaf plots are also helpful in summarizing the distribution and should be used before bivariate density estimation is attempted. For the case of n large, Carr et al (1987) suggest showing the density of points.…”

Section: Bivariate and Trivariate Datamentioning

confidence: 99%

Multivariate Data Visualization

McLeod

Provost

2001

Encyclopedia of Environmetrics

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Multivariate data visualization is an exciting area of current research by statisticians, engineers and those involved in data mining. This article first gives a brief overview of the most important current software available for data visualization and then discusses the general principles or philosophy involved in data visualization. This is followed by a discussion of some of the most interesting and useful multivariate graphical techniques.

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Scatterplot Matrix Techniques for Large N

Cited by 195 publications

References 11 publications

Continuous Learning Graphical Knowledge Unit for Cluster Identification in High Density Data Sets

Continuous Learning Graphical Knowledge Unit for Cluster Identification in High Density Data Sets

Exploratory Visualization with Multiple Linked Views

Multivariate Data Visualization

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