An visualization of multidimensional nonlinear structures of multivariatedata for a qualitative model choice are considered. These problems appear in technological, pharmaceutical, financial, medical, etc., industrie...
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ISBN:
(纸本)0780344235
An visualization of multidimensional nonlinear structures of multivariatedata for a qualitative model choice are considered. These problems appear in technological, pharmaceutical, financial, medical, etc., industries. It is showed that conventional methods of data analysis and model choice could be essentially reinforced by means of more effective use of human vision abilities. The pictorial analysis is developed as a human - computer dialog to search "pictorial" data representations which allow an effective detection of informative features and dependencies. Basic problems of this approach are discussed: the choice of perspective class of pictorial representation for different data sets, the interactive search of representations which ones allow to detect and describe visually acceptable solution of problem ( pattern recognition, clustering), the estimation of possible solution time and complexity for different data structures, etc. Practical implementations showing a high effectiveness of pictorial methods are demonstrated in application to a gas-turbine engine diagnostics by non stationary parameter trends, for an simultaneous monitoring of a hundred parameter dynamics of a power plant and for search of structure-activity relationship (QSAR) for new organic compounds.
multivariate data visualization is an interesting research field with many applications in ubiquitous fields of sciences. Radial visualization (Radviz) is one of the most common information visualization techniques fo...
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ISBN:
(纸本)9781450348157
multivariate data visualization is an interesting research field with many applications in ubiquitous fields of sciences. Radial visualization (Radviz) is one of the most common information visualization techniques for visualizing multivariatedata. Unfortunately, Radviz display different information about structures of multivariatedata on the different the order of the data dimensions and all points with different scale maps into the same point in the visual space. In this paper, we propose a method that improve the Radviz layout for class visualization of multivariatedata. The basic idea of our method is finding a good corner viewing of a hypercube. Our method provides an improvement visualizing class structures of multivariatedata sets on the Radviz. We present our method with two kinds of quality measurement. We prove the efficiency of our method for several data sets.
作者：
Yin, HJUniv Manchester
Inst Sci & Technol Dept Elect Engn & Elect Manchester M60 1QD Lancs England
When used for visualization of high-dimensional data, the self-organizing map (SOM) requires a coloring scheme such as the U-matrix to mark the distances between neurons. Even so, the structures of the data clusters m...
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When used for visualization of high-dimensional data, the self-organizing map (SOM) requires a coloring scheme such as the U-matrix to mark the distances between neurons. Even so, the structures of the data clusters may not be apparent and their shapes are often distorted. In this paper, a visualization-induced SOM (ViSOM) is proposed to overcome these shortcomings. The algorithm constrains and regularizes the inter-neuron distance with a parameter that controls the resolution of the map. The mapping preserves the inter-point distances of the input data on the map as well as the topology. It produces a graded mesh in the data space such that the distances between mapped data points on the map resemble those in the original space, like in the Sammon mapping. However, unlike the Sammon mapping, the ViSOM can accommodate both training data and new arrivals and is much simpler in computational complexity. Several experimental results and comparisons with other methods are presented.
In this work a review of multivariate data visualization and unsupervised modeling methods was presented, applied to DPV and LAPV signals of black, green, red and white tea infusions of China and Vietnam origin. It pr...
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In this work a review of multivariate data visualization and unsupervised modeling methods was presented, applied to DPV and LAPV signals of black, green, red and white tea infusions of China and Vietnam origin. It proves that graphical presentation by diagrams and charts has a number of advantages in comparison to analysis of the measured signals or numerical datasheets. The same data presented and modeled by different methods may deliver various, complementary conclusions and also the strategy verifies the quality of the experiments. Insight to the experimental data and extraction of the useful information were done applying Chernoff faces, star plots, radar plot and matrix scatterplot. Using SOMs and PCA it was demonstrated that DPV data enabled distinction of the tea kinds with some problems with separation of the green and white ones, while LAPV signals distinguished the origin of the infusions. Successful separation of the 8 different types of objects were done by polar dendrogram, first time applied in voltammetry.
multivariate volume data sets usually have complex interactions between fields, and features from different fields, such as segmented regions and isosurfaces, can be associated together to intuitively reveal the corre...
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multivariate volume data sets usually have complex interactions between fields, and features from different fields, such as segmented regions and isosurfaces, can be associated together to intuitively reveal the correlation and difference between fields. In this paper, we present a visual analytic approach for interactive feature exploration. A graph-based representation, called FeatureNet, is designed to provide a full picture of major features extracted from each field. FeatureNet visually summarizes both the nesting and association relationships of major features in each variable, and serves as a navigation tool to guide data exploration. Case studies with three simulation data sets demonstrate the effectiveness and usefulness of FeatureNet, and it can help users better understand and inspect the nesting and correlation relationships between fields.
A well-known limitation of classic continuous treemaps is that they generally provide two (or at most a few) visual mappings for data variables apart from the hierarchical relationships. Typically, one variable maps t...
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ISBN:
(纸本)9781450356169
A well-known limitation of classic continuous treemaps is that they generally provide two (or at most a few) visual mappings for data variables apart from the hierarchical relationships. Typically, one variable maps to cell area;another maps to color. However, many data-centric tasks require human users to consider multiple variables simultaneously. The current work introduces the concept of equal-height, variable-width cells in treemaps, which affords the packing of multiple variables into the cell areas of the terminals of the hierarchy. We demonstrate how color and some largely width-invariant graphs can be utilized in the cell areas to add additional visual information in a multi-variate treemap. Examples come from machine learning and from finance applications.
One of the most noticeable issues of parallel coordinate visualization is how to quantitatively analyze density caused by polyline growth in a limited space on axes. The existing visualization tools only support the c...
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ISBN:
(纸本)9783319467719;9783319467702
One of the most noticeable issues of parallel coordinate visualization is how to quantitatively analyze density caused by polyline growth in a limited space on axes. The existing visualization tools only support the comparison among single dimensions and single ranges of polylines, which could face limitation in cases of complicated analytics. This paper proposes a new visual-query technique, named SumUp, for statistical analysis of multiple attributes of dimensions and multiple ranges of polylines. The methodology of SumUp is primarily based on developing dynamic queries using brushing operations to deliver summary stacked bars adaptive with parallel coordinates. Users can easily observe quantitative information from data patterns and compare multiple attributes over the density of polylines in the parallel coordinate visualization. Early experiments show that our proposed technique could potentially enhance the manipulation on parallel coordinates, showing by a typical case study.
Uncertainty is inherent in medical decision making and poses a challenge for intelligent technologies. This paper focuses on magnetic resonance spectra (MRS) for discrimination of brain tumour types and grades. Modell...
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Uncertainty is inherent in medical decision making and poses a challenge for intelligent technologies. This paper focuses on magnetic resonance spectra (MRS) for discrimination of brain tumour types and grades. Modelling of this type of high-dimensional data is commonly affected by uncertainty caused by the presence of outliers. multivariatedata clustering and visualization of MRS data is proposed using the GTM framework with basis functions comprising Student t-distributions in order to minimize the negative impact on the model from outliers. The effectiveness of this model on the MRS data is demonstrated empirically. (c) 2005 Elsevier Ltd. All rights reserved.
For many data analysis tasks, such as the formation of well-balanced groups for a fair race or collaboration in learning settings, the balancing between data attributes is at least as important as the actual values of...
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For many data analysis tasks, such as the formation of well-balanced groups for a fair race or collaboration in learning settings, the balancing between data attributes is at least as important as the actual values of items. At the same time, comparison of values is implicitly desired for these tasks. Even with statistical methods available to measure the level of balance, human judgment, and domain expertise plays an important role in judging the level of balance, and whether the level of unbalance is acceptable or not. Accordingly, there is a need for techniques that improve decision-making in the context of group formation that can be used as a visual complement to statistical analysis. This paper introduces a novel glyph-based visualization, PeaGlyph, which aims to support the understanding of balanced and unbalanced data structures, for instance by using a frequency format through countable marks and salient shape characteristics. The glyph was designed particularly for tasks of relevance for investigation of properties of balanced and unbalanced groups, such as looking-up and comparing values. Glyph-based visualization methods provide flexible and useful abstractions for exploring and analyzing multivariatedata sets. The PeaGlyph design was based on an initial study that compared four glyph visualization methods in a joint study, including two base glyphs and their variations. The performance of the novel PeaGlyph was then compared to the best "performers" of the first study through evaluation. The initial results from the study are encouraging, and the proposed design may be a good alternative to the traditional glyphs for depicting multivariatedata and allowing viewers to form an intuitive impression as to how balanced or unbalanced a set of objects are. Furthermore, a set of design considerations is discussed in context of the design of the glyphs.
In this design study, we present a visualization technique that segments patients' histories instead of treating them as raw event sequences, aggregates the segments using criteria such as the whole history or tre...
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In this design study, we present a visualization technique that segments patients' histories instead of treating them as raw event sequences, aggregates the segments using criteria such as the whole history or treatment combinations, and then visualizes the aggregated segments as static dashboards that are arranged in a dashboard network to show longitudinal changes. The static dashboards were developed in nine iterations, to show 15 important attributes from the patients' histories. The final design was evaluated with five non-experts, five visualization experts and four medical experts, who successfully used it to gain an overview of a 2,000 patient dataset, and to make observations about longitudinal changes and differences between two cohorts. The research represents a step-change in the detail of large-scale data that may be successfully visualized using dashboards, and provides guidance about how the approach may be generalized.
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