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54
Revealing structure within clustered parallel coordinates displays
 IN PROCEEDINGS OF THE 2005 IEEE SYMPOSIUM ON INFORMATION VISUALIZATION
, 2005
"... In order to gain insight into multivariate data, complex structures must be analysed and understood. Parallel coordinates is an excellent ..."
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Cited by 68 (5 self)
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In order to gain insight into multivariate data, complex structures must be analysed and understood. Parallel coordinates is an excellent
Visual clustering in parallel coordinates
 COMPUTER GRAPHICS FORUM
, 2008
"... Parallel coordinates have been widely applied to visualize highdimensional and multivariate data, discerning patterns within the data through visual clustering. However, the effectiveness of this technique on large data is reduced by edge clutter. In this paper, we present a novel framework to redu ..."
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Cited by 31 (6 self)
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Parallel coordinates have been widely applied to visualize highdimensional and multivariate data, discerning patterns within the data through visual clustering. However, the effectiveness of this technique on large data is reduced by edge clutter. In this paper, we present a novel framework to reduce edge clutter, consequently improving the effectiveness of visual clustering. We exploit curved edges and optimize the arrangement of these curved edges by minimizing their curvature and maximizing the parallelism of adjacent edges. The overall visual clustering is improved by adjusting the shape of the edges while keeping their relative order. The experiments on several representative datasets demonstrate the effectiveness of our approach.
Scattering points in parallel coordinates
 IEEE Trans. Vis. Comput. Graph
"... Abstract—In this paper, we present a novel parallel coordinates design integrated with points (Scattering Points in Parallel Coordinates, SPPC), by taking advantage of both parallel coordinates and scatterplots. Different from most multiple views visualization frameworks involving parallel coordina ..."
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Cited by 27 (7 self)
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Abstract—In this paper, we present a novel parallel coordinates design integrated with points (Scattering Points in Parallel Coordinates, SPPC), by taking advantage of both parallel coordinates and scatterplots. Different from most multiple views visualization frameworks involving parallel coordinates where each visualization type occupies an individual window, we convert two selected neighboring coordinate axes into a scatterplot directly. Multidimensional scaling is adopted to allow converting multiple axes into a single subplot. The transition between two visual types is designed in a seamless way. In our work, a series of interaction tools has been developed. Uniform brushing functionality is implemented to allow the user to perform data selection on both points and parallel coordinate polylines without explicitly switching tools. A GPU accelerated Dimensional Incremental Multidimensional Scaling (DIMDS) has been developed to significantly improve the system performance. Our case study shows that our scheme is more efficient than traditional multiview methods in performing visual analysis tasks.
Parallel Edge Splatting for Scalable Dynamic Graph Visualization
, 2011
"... We present a novel dynamic graph visualization technique based on nodelink diagrams. The graphs are drawn sidebyside from left to right as a sequence of narrow stripes that are placed perpendicular to the horizontal time line. The hierarchically organized vertices of the graphs are arranged on v ..."
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Cited by 23 (6 self)
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We present a novel dynamic graph visualization technique based on nodelink diagrams. The graphs are drawn sidebyside from left to right as a sequence of narrow stripes that are placed perpendicular to the horizontal time line. The hierarchically organized vertices of the graphs are arranged on vertical, parallel lines that bound the stripes; directed edges connect these vertices from left to right. To address massive overplotting of edges in huge graphs, we employ a splatting approach that transforms the edges to a pixelbased scalar field. This field represents the edge densities in a scalable way and is depicted by nonlinear color mapping. The visualization method is complemented by interaction techniques that support data exploration by aggregation, filtering, brushing, and selective data zooming. Furthermore, we formalize graph patterns so that they can be interactively highlighted on demand. A case study on software releases explores the evolution of call graphs extracted from the JUnit open source software project. In a second application, we demonstrate the scalability of our approach by applying it to a bibliography dataset containing more than 1.5 million paper titles from 60 years of research history producing a vast amount of relations between title words.
Continuous Parallel Coordinates
, 2009
"... Typical scientific data is represented on a grid with appropriate interpolation or approximation schemes, defined on a continuous domain. The visualization of such data in parallel coordinates may reveal patterns latently contained in the data and thus can improve the understanding of multidimensio ..."
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Cited by 18 (7 self)
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Typical scientific data is represented on a grid with appropriate interpolation or approximation schemes, defined on a continuous domain. The visualization of such data in parallel coordinates may reveal patterns latently contained in the data and thus can improve the understanding of multidimensional relations. In this paper, we adopt the concept of continuous scatterplots for the visualization of spatially continuous input data to derive a density model for parallel coordinates. Based on the point–line duality between scatterplots and parallel coordinates, we propose a mathematical model that maps density from a continuous scatterplot to parallel coordinates and present different algorithms for both numerical and analytical computation of the resulting density field. In addition, we show how the 2D model can be used to successively construct continuous parallel coordinates with an arbitrary number of dimensions. Since continuous parallel coordinates interpolate data values within grid cells, a scalable and dense visualization is achieved, which will be demonstrated for typical multivariate scientific data.
Evaluation of Cluster Identification Performance for Different PCP Variants
, 2010
"... Parallel coordinate plots (PCPs) are a wellknown visualization technique for viewing multivariate data. In the past, various visual modifications to PCPs have been proposed to facilitate tasks such as correlation and cluster identification, to reduce visual clutter, and to increase their informatio ..."
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Cited by 18 (0 self)
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Parallel coordinate plots (PCPs) are a wellknown visualization technique for viewing multivariate data. In the past, various visual modifications to PCPs have been proposed to facilitate tasks such as correlation and cluster identification, to reduce visual clutter, and to increase their information throughput. Most modifications pertain to the use of color and opacity, smooth curves, or the use of animation. Although many of these seem valid improvements, only few user studies have been performed to investigate this, especially with respect to cluster identification. We performed a user study to evaluate cluster identification performance – with respect to response time and correctness – of nine PCP variations, including standard PCPs. To generate the variations, we focused on covering existing techniques as well as possible while keeping testing feasible. This was done by adapting and merging techniques, which led to the following novel variations. The first is an effective way of embedding scatter plots into PCPs. The second is a technique for highlighting fuzzy clusters based on neighborhood density. The third is a splinebased drawing technique to reduce ambiguity. The last is a pair of animation schemes for PCP rotation. We present an overview of the tested PCP variations and the results of our study. The most important result is that a fair number of the seemingly valid improvements, with the exception of scatter plots embedded into PCPs, do not result in significant performance gains.
Angular histograms: Frequencybased visualizations for large, high dimensional data
 IEEE Transactions on Visualization and Computer Graphics
"... Fig. 1. This figure shows the angular histogram and the attribute curves of the animal tracking data set. Color is mapped to the data density. Red indicates the largest frequency and light blue the smallest. Abstract—Parallel coordinates are a popular and wellknown multivariate data visualization t ..."
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Cited by 12 (3 self)
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Fig. 1. This figure shows the angular histogram and the attribute curves of the animal tracking data set. Color is mapped to the data density. Red indicates the largest frequency and light blue the smallest. Abstract—Parallel coordinates are a popular and wellknown multivariate data visualization technique. However, one of their inherent limitations has to do with the rendering of very large data sets. This often causes an overplotting problem and the goal of the visual information seeking mantra is hampered because of a cluttered overview and noninteractive update rates. In this paper, we propose two novel solutions, namely, angular histograms and attribute curves. These techniques are frequencybased approaches to large, highdimensional data visualization. They are able to convey both the density of underlying polylines and their slopes. Angular histogram and attribute curves offer an intuitive way for the user to explore the clustering, linear correlations and outliers in large data sets without the overplotting and clutter associated with traditional parallel coordinates. We demonstrate the results on a wide variety of data sets including realworld, highdimensional biological data. Finally, we compare our methods with the other popular frequencybased algorithms.
Guided analysis of hurricane trends using statistical processes integrated with interactive parallel coordinates
 In Proc. IEEE Symposium on Visual Analytics Science and Technology (VAST
, 2009
"... This paper demonstrates the promise of augmenting interactive multivariate representations with information from statistical processes in the domain of weather data analysis. Statistical regression, correlation analysis, and descriptive statistical calculations are integrated via graphical indicator ..."
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Cited by 11 (1 self)
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This paper demonstrates the promise of augmenting interactive multivariate representations with information from statistical processes in the domain of weather data analysis. Statistical regression, correlation analysis, and descriptive statistical calculations are integrated via graphical indicators into an enhanced parallel coordinates system, called the Multidimensional Data eXplorer (MDX). These statistical indicators, which highlight significant associations in the data, are complemented with interactive visual analysis capabilities. The resulting system allows a smooth, interactive, and highly visual workflow. The system’s utility is demonstrated with an extensive hurricane climate study that was conducted by a hurricane expert. In the study, the expert used a new data set of environmental weather data, composed of 28 independent variables, to predict annual hurricane activity. MDX shows the Atlantic Meridional Mode increases the explained variance of hurricane seasonal activity by 715 % and removes less significant variables used in earlier studies. The findings and feedback from the expert (1) validate the utility of the data set for hurricane prediction, and (2) indicate that the integration of statistical processes with interactive parallel coordinates, as implemented in MDX, addresses both deficiencies in traditional weather data analysis and exhibits some of the expected benefits of visual data analysis.
Splatting the lines in parallel coordinates
 COMPUTER GRAPHICS FORUM/IEEEVGTC SYMPOSIUM ON VISUALIZATION
, 2009
"... In this paper, we propose a novel splatting framework for clutter reduction and pattern revealing in parallel coordinates. Our framework consists of two major components: a polyline splatter for cluster detection and a segment splatter for clutter reduction. The cluster detection is performed by spl ..."
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Cited by 10 (3 self)
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In this paper, we propose a novel splatting framework for clutter reduction and pattern revealing in parallel coordinates. Our framework consists of two major components: a polyline splatter for cluster detection and a segment splatter for clutter reduction. The cluster detection is performed by splatting the lines one by one into the parallel coordinates plots, and for each splatted line we enhance its neighboring lines and suppress irrelevant ones. To reduce visual clutter caused by line crossings and overlappings in the clustered results, we provide a segment splatter which represents each polyline by one segment and splats these segments with different speeds, colors, and lengths from the leftmost axis to the rightmost axis. Users can interactively control both the polyline splatting and the segment splatting processes to emphasize the features they are interested in. The experimental results demonstrate that our framework can effectively reveal some hidden patterns in parallel coordinates.
SpringView: cooperation of Radviz and parallel coordinates for view optimization and clutter reduction. In: International conference on coordinated and multiple views in exploratory visualization
"... In this paper we integrate radviz and parallel coordinates, two methods able to handle multidimensional datasets, exploiting their contrasting characteristics. From on side radviz offers good direct data manipulation (i.e., brushing) techniques and low cluttering but it fails in providing visualiz ..."
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Cited by 9 (1 self)
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In this paper we integrate radviz and parallel coordinates, two methods able to handle multidimensional datasets, exploiting their contrasting characteristics. From on side radviz offers good direct data manipulation (i.e., brushing) techniques and low cluttering but it fails in providing visualization of quantitative information; conversely, parallel coordinates clearly shows the values of data attributes and their ranges but suffers from high cluttering also on small datasets and presents tedious manipulation techniques. We developed a prototype, called SpringView, that allows for simultaneously viewing both radviz and parallel coordinates and implements several useful techniques to manipulate the data, both interactively and, more interestingly, automatically. We challenged our approach against two well know multidimensional datasets, proving its effectiveness. 1