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A Space-Filling multidimensional visualization (SFMDVis) for Exploratory data Analysis

INFORMATION SCIENCES 2017年 390卷 32-53页

作者： Huang, Tze-Haw Huang, Mao Lin Quang Vinh Nguyen Zhao, Laiping Huang, Weidong Chen, Jinjun Univ Technol Sydney Sch Software FEIT POB 123 Broadway NSW 2007 Australia Univ Western Sydney Locked Bag 1797 South Penrith Dc NSW 1797 Australia Tianjin Univ Sch Comp Software China 92 Weijin Rd Tianjin Peoples R China Univ Tasmania Sch Engn & ICT Newnham Tas 7248 Australia

The space-filling visualization model was first invented by Ben Shneiderman [28] for maximizing the utilization of display space in relational data (or graph) visualization, especially for tree visualization. It uses the concept of Enclosure which dismisses the "edges" in the graphic representation that are all too frequently used in traditional node-link based graph visualizations. Therefore, the major issue in graph visualization which is the edge crossing can be naturally solved through the adoption of a space filling approach. However in the past, the space-filling concept has not attracted much attention from researchers in the field of multidimensional visualization. Although the problem of 'edge crossing' has also occurred among polylines which are used as the basic visual elements in the parallel coordinates visualization, it is problematic if those 'edge crossings' among polylines are not evenly distributed on the display plate as visual clutter will occur. This problem could significantly reduce the human readability in terms of reviewing a particular region of the visualization. In this study, we propose a new Space-Filling multidimensional data visualization (SFMDVis) that for the first-time introduces a space-filling approach into multidimensional data visualization. The main contributions are: (1) achieving the maximization of space utilization in multidimensional visualization (i.e. 100% of the display area is fully used), (2) eliminating visual clutter in SFMDVis through the use of the non-classic geometric primitive and (3) improving the quality of visualization for the visual perception of linear correlations among different variables as well as recognizing data patterns. To evaluate the quality of SFMDVis, we have conducted a usability study to measure the performance of SFMDVis in comparison with parallel coordinates and a scatterplot matrix for finding linear correlations and data patterns. The evaluation results have suggested that the accuracy

关键词： multidimensional data visualization Space-filling data visualization Visual data analysis Linear correlation data pattern Usability study

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Adjustable general line coordinates for visual knowledge discovery in n-D data

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INFORMATION visualization 2019年第1期18卷 3-32页

作者： Kovalerchuk, Boris Grishin, Vladimir Cent Washington Univ 400 East Univ Way Ellensburg WA 98926 USA View Trends Ltd Port St Lucie FL USA

Preserving all multidimensional data in two-dimensional visualization is a long-standing problem in Visual Analytics, Machine Learning/data Mining, and Multiobjective Pareto Optimization. While Parallel and Radial (Star) coordinates preserve all n-D data in two dimensions, they are not sufficient to address visualization challenges of all possible datasets such as occlusion. More such methods are needed. Recently, the concepts of lossless General Line Coordinates that generalize Parallel, Radial, Cartesian, and other coordinates were proposed with initial exploration and application of several subclasses of General Line Coordinates such as Collocated Paired Coordinates and Star Collocated Paired Coordinates. This article explores and enhances benefits of General Line Coordinates. It shows the ways to increase expressiveness of General Line Coordinates including decreasing occlusion and simplifying visual pattern while preserving all n-D data in two dimensions by adjusting General Line Coordinates for given n-D datasets. The adjustments include relocating, rescaling, and other transformations of General Line Coordinates. One of the major sources of benefits of General Line Coordinates relative to Parallel Coordinates is twice less number of point and lines in visual representation of each n-D points. This article demonstrates the benefits of different General Line Coordinates for real data visual analysis such as health monitoring and benchmark Iris data classification compared with results from Parallel Coordinates, Radvis, and Support Vector Machine. The experimental part of the article presents the results of the experiment with about 70 participants on efficiency of visual pattern discovery using Star Collocated Paired Coordinates, Parallel, and Radial Coordinates. It shows advantages of visual discovery of n-D patterns using General Line Coordinates subclass Star Collocated Paired Coordinates with n = 160 dimensions.

关键词： multidimensional data visualization knowledge discovery visual data mining machine learning general line coordinates lossless visual representation reversible visual representation adjustable coordinates clutter reduction parallel coordinates

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L-plotting-A method for visual analysis of physiological experimental and modeling multi-component data

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NEUROCOMPUTING 2010年第1-3期74卷 328-336页

作者： Dunin-Barkowski, W. L. Lovering, A. T. Orem, J. M. Baekey, D. M. Dick, T. E. Rybak, I. A. Morris, K. F. O'Connor, R. Nuding, S. C. Shannon, R. Lindsey, B. G. Russian Acad Sci Dept Neuroinformat Sci Res Inst Syst Anal Moscow Russia TTU HSC Dept Cell Physiol & Biophys Sch Med Lubbock TX USA Univ Oregon Dept Human Physiol Eugene OR 97403 USA Case Western Reserve Univ Dept Physiol Cleveland OH 44106 USA Drexel Univ Dept Bio Med Engn Philadelphia PA 19104 USA Univ S Florida Dept Mol Pharmacol & Physiol Sch Biomed Sci Coll Med Tampa FL USA Case Western Reserve Univ Dept Med Neurosci Cleveland OH 44106 USA Drexel Univ Coll Med Dept Neurobiol & Anat Philadelphia PA 19104 USA

A method for visualization of dynamic multidimensional data-L-plotting similar to recurrence plotting is described For multi-neuronal brainstem recordings the method demonstrates that the neural respiratory pattern generator (RPG) switches between the two phases inspiratory and expiratory The method helps to mark phase switching moments and to characterize the pattern of the RPG restart after temporary cessation of rhythmicity Comparison of L-plots for experimental data and network simulations helps verification of computational models (C) 2010 Elsevier B V All rights reserved

关键词： multidimensional data visualization Recurrence plotting Multi neuronal recordings Brainstem respiratory pattern generator Verification of computational models

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HOW TO MAKE SAMMON MAPPING USEFUL FOR multidimensional data-STRUCTURES ANALYSIS

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PATTERN RECOGNITION 1994年第7期27卷 949-959页

作者： DZWINEL, W Institute of Computer Science AGH al. Mickiewicza 30 30-059 Cracow Poland

The ways of optimization of Sammon's mapping technique are suggested. Two sorts of the mapping artifacts produced by the local minimum traps and non-coherence of the source and target spaces (i.e. multi- and low-dimensional ones, respectively) are discussed. The methods of reduction of the artifacts' influence on the resulting two- and three-dimensional patterns are proposed. The nuclear reactor diagnostics system is taken as the source of real multidimensional data. Usefulness of the mapping for their analysis is shown.

关键词： multidimensional data visualization SAMMONS CRITERION MAPPING OF PATTERNS ARTIFACTS SIMULATED ANNEALING CLUSTERING NUCLEAR REACTOR DIAGNOSTICS

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Interactive focus plus context analysis of large, time-dependent flow simulation data

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SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL 2006年第12期82卷 851-865页

作者： Doleisch, Helmut Hauser, Helwig Gasser, Martin Kosara, Robert VRVis Res Ctr SimVis Project A-1220 Vienna Austria

The visualization of time-dependent simulation data, such as data sets from computational fluid dynamics (CFD) simulation, is still a very challenging task. In this paper, we present a new approach for the interactive visual analysis of flow simulation data, which is especially targeted at the analysis of time-dependent data. This supports the flexible specification and visualization of flow features in an interactive setup of multiple linked views. Special emphasis is put on new mechanisms to capture time-dependent features (i.e. flow features that are inherently dependent on time). We propose the integration of attribute derivation into the process of interactive visual analysis to enable the subsequent user access to otherwise implicit properties of the unsteady data in our interactive feature specification framework. All views of this flow analysis setup are linked, in the sense that the features in focus are consistently emphasized in the visualization (more colorful, less transparent) whereas the rest of the data are only shown as context in reduced style. In addition to introducing our new approach, we also demonstrate its use in the context of several application examples.

关键词： interactive feature specification time-dependent features focus plus context visualization time-dependent flow visualization multidimensional data visualization

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Taggle: Combining overview and details in tabular data visualizations

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INFORMATION visualization 2020年第2期19卷 114-136页

作者： Furmanova, Katarina Gratzl, Samuel Stitz, Holger Zichner, Thomas Jaresova, Miroslava Lex, Alexander Streit, Marc Masaryk Univ Brno Czech Republic Datavisyn GmbH Linz Austria Johannes Kepler Univ Linz Linz Austria Boehringer Ingelheim RCV GmbH & Co KG Vienna Austria Czechitas As Prague Czech Republic Univ Utah Salt Lake City UT USA

Most tabular data visualization techniques focus on overviews, yet many practical analysis tasks are concerned with investigating individual items of interest. At the same time, relating an item to the rest of a potentially large table is important. In this work, we present Taggle, a tabular visualization technique for exploring and presenting large and complex tables. Taggle takes an item-centric, spreadsheet-like approach, visualizing each row in the source data individually using visual encodings for the cells. At the same time, Taggle introduces data-driven aggregation of data subsets. The aggregation strategy is complemented by interaction methods tailored to answer specific analysis questions, such as sorting based on multiple columns and rich data selection and filtering capabilities. We demonstrate Taggle by a case study conducted by a domain expert on complex genomics data analysis for the purpose of drug discovery.

关键词： visualization techniques tabular data multidimensional data visualization aggregation hierarchical grouping and sorting degree of interest focus and context

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Visualizing high density clusters in multidimensional data using optimized star coordinates

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COMPUTATIONAL STATISTICS 2011年第4期26卷 655-678页

作者： Tran Van Long Linsen, Lars Univ Transport & Commun Hanoi Vietnam Jacobs Univ Sch Engn & Sci Bremen Germany

multidimensional multivariate data have been studied in different areas for quite some time. Commonly, the analysis goal is not to look into individual records but to understand the distribution of the records at large and to find clusters of records that exhibit correlations between dimensions or variables. We propose a visualization method that operates on density rather than individual records. To not restrict our search for clusters, we compute density in the given multidimensional space. Clusters are formed by areas of high density. We present an approach that automatically computes a hierarchical tree of high density clusters. For visualization purposes, we propose a method to project the multidimensional clusters to a 2D or 3D layout. The projection method uses an optimized star coordinates layout. The optimization procedure minimizes the overlap of projected clusters and maximally maintains the cluster shapes, compactness, and distribution. The star coordinate visualization allows for an interactive analysis of the distribution of clusters and comprehension of the relations between clusters and the original dimensions. Clusters are being visualized using nested sequences of density level sets leading to a quantitative understanding of information content, patterns, and relationships.

关键词： multidimensional data visualization Density estimation Hierarchical clustering Star coordinates

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Evaluation on interactive visualization data with scatterplots

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Visual Informatics 2020年第4期4卷 1-10页

作者： Quang Vinh Nguyen Natalie Miller David Arness Weidong Huang Mao Lin Huang Simeon Simoff MARCS Institute Western Sydney UniversityAustralia School of Computer Data and Mathematical SciencesWestern Sydney UniversityAustralia School of Psychology Western Sydney UniversityAustralia Faculty of Transdisciplinary Innovation University of TechnologySydneyAustralia School of Software Faculty of Engineering&ITUniversity of TechnologySydneyAustralia

Scatterplots and scatterplot matrix methods have been popularly used for showing statistical graphics and for exposing patterns in multivariate data.A recent technique,called Linkable Scatterplots,provides an interesting idea for interactive visual exploration which provides a set of necessary plot panels on demand together with interaction,linking and *** article presents a controlled study with a mixed-model design to evaluate the effectiveness and user experience on the visual exploration when using a Sequential-Scatterplots who a single plot is shown at a time,Multiple-Scatterplots who number of plots can be specified and shown,and Simultaneous-Scatterplots who all plots are shown as a scatterplot *** from the study demonstrated higher accuracy using the Multiple-Scatterplots visualization,particularly in comparison with the *** the time taken to complete tasks was longer in the Multiple-Scatterplots technique,compared with the simpler Sequential-Scatterplots,Multiple-Scatterplots is inherently more ***,the Multiple-Scatterplots technique is the most highly preferred and positively experienced technique in this ***,results support the strength of Multiple-Scatterplots and highlight its potential as an effective data visualization technique for exploring multivariate data.

关键词： Multivariate data visualization multidimensional data visualization Scatterplots Scatterplot matrix Controlled study

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3D parallel coordinate systems -: A new data visualization method in the context of microscopy-based multicolor tissue cytometry

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CYTOMETRY PART A 2006年第7期69A卷 601-611页

作者： Streit, Marc Ecker, Rupert C. Österreicher, Katja Steiner, Georg E. Bischof, Horst Bangert, Christine Kopp, Tamara Rogojanu, Radu TissueGnost GMBH A-1020 Vienna Austria Graz Univ Technol Inst Comp Graph & Vis A-8010 Graz Austria Med Univ Vienna Dept Dermatol DIAID Vienna Austria Med Univ Vienna Dept Urol Vienna Austria

Background: Presentation of multiple interactions is of vital importance in the new field of cytomics. Quantitative analysis of multi- and polychromatic stained cells in tissue will serve as a basis for medical diagnosis and prediction of disease in forthcoming years. A major problem associated with huge interdependent data sets is visualization. Therefore, alternative and easy-to-handle strategies for data visualization as well as data meta-evaluation (population analvsis. cross-correlation, co-expression analysis) were developed. Methods: To facilitate human comprehension of complex data, 3D parallel coordinate systems have been developed and used in automated microscopy-based multicolor tissue cytometry ((MMTC). Frozen sections of human skin were stained using the combination anti-CD45-PE, anti-CD14-APC, and SytoxGreen as well as the appropriate single and double negative controls. Stained sections were analyzed using automated confocal laser microscopy and semiquantitative MMTC-analysis with TissueQuest 2.0. The 3D parallel coordinate plots are generated from semiquantitative immunofluorescent data of single cells. The 2D and 3D parallel coordinate plots were produced by further processing using the Matlab environment (Mathworks, USA). Results: Current techniques in data visualization primarily utilize scattergrams, where two parameters are plotted against each other on Linear or logarithmic scales. However, data evaluation on cartesian x/y-scattergrams is, in general, only of limited value in multiparameter analysis. Dot plots suffer from serious problems, and in particular, do not meet the requirements of polychromatic high-context tissue cytometry of millions of cells. The 3D parallel coordinate plot replaces the vast amount of scattergrams that are usually needed for the cross-correlation analysis. As a result, the scientist is able to perform the data metaevaluation by using one single plot. On the basis of 2D parallel coordinate systems, a density isosurfa

关键词： microscopy-based multicolor tissue cytometry multicolor staining 3D parallel coordinate systems multidimensional data visualization scattergrams

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Brain Activity is Influenced by How High Dimensional data are Represented: An EEG Study of Scatterplot Diagnostic (Scagnostics) Measures

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JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2024年第1期8卷 19-49页

作者： Etemadpour, Ronak Shintree, Sonali Shereen, A. Duke Verus Res 6100 Uptown Blvd NESuite 260 Albuquerque NM 87110 USA UNM Hlth Sci Ctr UNM Radiol Dept Albuquerque NM USA CUNY City Coll 160 Convent Ave New York NY 10031 USA CUNY Grad Ctr Neurosci Initiat Adv Sci Res Ctr New York NY 10031 USA

visualization and visual analytic tools amplify one's perception of data, facilitating deeper and faster insights that can improve decision making. For multidimensional data sets, one of the most common approaches of visualization methods is to map the data into lower dimensions. Scatterplot matrices (SPLOM) are often used to visualize bivariate relationships between combinations of variables in a multidimensional dataset. However, the number of scatterplots increases quadratically with respect to the number of variables. For high dimensional data, the corresponding enormous number of scatterplots makes data exploration overwhelmingly complex, thereby hindering the usefulness of SPLOM in human decision making processes. One approach to address this difficulty utilizes Graph-theoretic Scatterplot Diagnostic (Scagnostics) to automatically extract a subset of scatterplots with salient features and of manageable size with the hope that the data will be sufficient for improving human decisions. In this paper, we use Electroencephalogram (EEG) to observe brain activity while participants make decisions informed by scatterplots created using different visual measures. We focused on 4 categories of Scagnostics measures: Clumpy, Monotonic, Striated, and Stringy. Our findings demonstrate that by adjusting the level of difficulty in discriminating between data sets based on the Scagnostics measures, different parts of the brain are activated: easier visual discrimination choices involve brain activity mostly in visual sensory cortices located in the occipital lobe, while more difficult discrimination choices tend to recruit more parietal and frontal regions as they are known to be involved in resolving ambiguities. Our results imply that patterns of neural activity are predictive markers of which specific Scagnostics measures most assist human decision making based on visual stimuli such as ours.

关键词： multidimensional data visualization Scagnostic measures EEG ERP Cognition Brain activities Scatterplot features

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