When used for visualization of high dimensional data, the self-organizing maps (SOM) requires a coloring scheme, or interpolation, or applying some projection techniques to analyze the intrinsic structure of the data....
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ISBN:
(纸本)3540287523
When used for visualization of high dimensional data, the self-organizing maps (SOM) requires a coloring scheme, or interpolation, or applying some projection techniques to analyze the intrinsic structure of the data. Even so, the structures of the data clusters may not be apparent and their shapes are often distorted. In order to overcome some shortcomings of the traditional SOM visualization method a novel technique is presented in this paper. Several experimental data sets including the chain-link problem and IRIS data are used to test the approach. The analysis results prove that the presented technique provides a better picture of the high dimensional data to understand their intrinsic structure.
The Milestones Project is a comprehensive attempt to collect, document, illustrate, and interpret the historical developments leading to modern datavisualization and visual thinking. This paper provides an overview a...
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ISBN:
(纸本)3540256776
The Milestones Project is a comprehensive attempt to collect, document, illustrate, and interpret the historical developments leading to modern datavisualization and visual thinking. This paper provides an overview and brief tour of the milestones content, with a few illustrations of significant contributions to the history of datavisualization. This forms one basis for exploring interesting questions and problems in the use of statistical and graphical methods to explore this history, a topic that can be called "statistical historiography".
As data continue to grow beyond enormous sizes, visualization in three dimensions becomes the preferred way to analyze and solve complex problems. When visualization techniques are combined with technology that allows...
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data repositories around the world hold many thousands of datasets. A problem for remote dataset users is to browse the repositories and efficiently locate relevant datasts. In this note, we introduce the Iconic Remot...
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ISBN:
(纸本)081945642X
data repositories around the world hold many thousands of datasets. A problem for remote dataset users is to browse the repositories and efficiently locate relevant datasts. In this note, we introduce the Iconic Remote Visual data Exploration tool (IRVDX), which provides visual browsing for exploring the features of remote and distributed data without the necessity of downloading the entire dataset. IRVDX employs three kinds of visualizations: one provides a reduced representation of the datasets, which we call dataset Icons. These icons show the important statistical characteristics of datasets and help to identify relevant datasets from distributed repositories. Another one is called the Remote dataset Visual Browser that provides visualizations to browse remote data without downloading the complete dataset to identify its content. The final one provides visualizations to show the degree of similarity between two datasets and to visually determine whether a join of two remote datasets will be meaningful. In this paper, we describe the design and implementation of IRVDX in detail. We assess the benefits of our dataset Icons against the traditional text-based interfaces and show the usefulness of IRVDX by conducting experiments with datasets from the UCI KDD Archive.
In this paper, we investigate a method of using principal component analysis(PCA) to fit an encapsulating ellipse to the image of a hypothetically ellipsoidal object. This technique is aimed at applications such as fr...
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ISBN:
(纸本)8090310079
In this paper, we investigate a method of using principal component analysis(PCA) to fit an encapsulating ellipse to the image of a hypothetically ellipsoidal object. This technique is aimed at applications such as fruit sorting, where resource constraints and speed requirements necessitate the approximation of data. Copyright UNION Agency-Science Press.
The proceedings contain 13 papers. The topics discussed include: coordinated parallel views for the exploratory analysis of microarray time-course data;MUSA-a prototype for multiple-step aggregation visualization;spri...
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ISBN:
(纸本)076952396X
The proceedings contain 13 papers. The topics discussed include: coordinated parallel views for the exploratory analysis of microarray time-course data;MUSA-a prototype for multiple-step aggregation visualization;spring view: cooperation of Radviz and parallel coordinates for view optimization and clutter reduction;multiple views to support engineering change management for complex products;visual dataanalysis using tracked statistical measures within parallel coordinate representations;interactive visual analysis of multi-parameter families of function graphs;exploration of dimensionality reduction for text visualization;a collaborative multi-view virtual environment for molecular visualization and modeling;prism: a multi-view visualization tool for multi-physics simulation;interactive exploration of unsteady 3-D flow with linked 2-D/3-D texture advection;and space-scale animation: enhancing cross-scale understanding of multiscale structures in multiple views.
Scientific visualization serves as a useful technique for rapid analysis of large data sets. The application of visualization techniques to physical simulations executed on high-performance computing hardware offers t...
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With our increasing ability to capture or produce and to store large multivariate data, these data sets are increasing both in size and complexity. Many conventional techniques for visualizing multivariate data suffer...
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ISBN:
(纸本)076952396X
With our increasing ability to capture or produce and to store large multivariate data, these data sets are increasing both in size and complexity. Many conventional techniques for visualizing multivariate data suffer from problems like cluttered displays since they are not designed to handle these amounts of entries. We present a novel method to overcome this problem by interactively selecting and displaying statistics derived from the data in a separate view. Changes in the display are visually tracked by animation and vector plotting for easy comparison of various measures applied to different subsets of the data.
The Self-Organizing Map is one of most prominent tools for the analysis and visualization of high-dimensional data. We propose a novel visualization technique for Self-Organizing Maps which can be displayed either as ...
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ISBN:
(纸本)3540260765
The Self-Organizing Map is one of most prominent tools for the analysis and visualization of high-dimensional data. We propose a novel visualization technique for Self-Organizing Maps which can be displayed either as a vector field where arrows point to cluster centers, or as a plot that stresses cluster borders. A parameter is provided that allows for visualization of the cluster structure at different levels of detail. Furthermore, we present a number of experimental results using standard data mining benchmark data.
We describe the visualization of high dimensional marketing data for a financial asset management company. The data typically consists of 30 to a 100 variables of 25000 to half a million clients. We use the visualizat...
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ISBN:
(纸本)0769523978
We describe the visualization of high dimensional marketing data for a financial asset management company. The data typically consists of 30 to a 100 variables of 25000 to half a million clients. We use the visualization of the correlation matrix as a variable selection tool which makes it easier to find patterns in the data. The user can then select data ranges of the selected variables and start a cluster analysis using 5 variables. The clustered data are then visualized as a set of spheres. In an additional visualization we first sort data values of a client variable and then visualize the sorted cubic data in a cube using volume rendering and isosurfaces. The interactive correlation visualization allows marketing researchers to quickly explore all kinds of combinations of variables, which enables them to find valuable client behavior patterns much faster. The cluster visualization allowed researchers to identify detailed groups of customer with similar behavior. Additionally, the visualization of the sorted cubic data gives in dept information of one variable over the total sample of customers. With these visualizations, a better understanding is given on customer behavior.
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