Progressive parallel coordinates

Rosenbaum, René; Zhi, Jian; Hamann, Bernd

doi:10.1109/pacificvis.2012.6183570

Cited by 20 publications

(13 citation statements)

References 18 publications

(26 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is by no means a simple task and adapting existing techniques to the PVA concept is a complex research question in its own regard, very similar to the problem of parallelizing existing algorithms for their distributed use. For some techniques, progressive variants have already been proposed -for example, for k-Means [52], MDS [53], t-SNE [54], Treemaps [55], and Parallel Coordinates [56,57]. Yet these are merely a tiny fraction of the analysis and visualization techniques our community are accustomed to use.…”

Section: Discussionmentioning

confidence: 99%

A Review and Characterization of Progressive Visual Analytics

et al. 2018

View full text Add to dashboard Cite

Progressive Visual Analytics (PVA) has gained increasing attention over the past years. It brings the user into the loop during otherwise long-running and non-transparent computations by producing intermediate partial results. These partial results can be shown to the user for early and continuous interaction with the emerging end result even while it is still being computed. Yet as clear-cut as this fundamental idea seems, the existing body of literature puts forth various interpretations and instantiations that have created a research domain of competing terms, various definitions, as well as long lists of practical requirements and design guidelines spread across different scientific communities. This makes it more and more difficult to get a succinct understanding of PVA's principal concepts, let alone an overview of this increasingly diverging field. The review and discussion of PVA presented in this paper address these issues and provide (1) a literature collection on this topic, (2) a conceptual characterization of PVA, as well as (3) a consolidated set of practical recommendations for implementing and using PVA-based visual analytics solutions.Keywords: visual analytics; progressive visualization; incremental visualization; online algorithms MotivationWith data growing in size and complexity, and analysis methods getting more sophisticated and computationally intensive, the idea of Progressive Visual Analytics (PVA) [1,2] becomes increasingly appealing. A PVA approach can either subdivide the data to process each data chunk individually, or it can subdivide the analytic process into computational steps that iteratively refine analytic results [3]. By doing so, PVA yields partial results of increasing completeness or approximative results of increasing correctness, respectively. This is useful in a wide range of visual analytics scenarios:• to realize responsive client-server visualizations using incremental data transmissions Because of its versatility, the progressive approach to data analysis and visualization is alternatively seen as a paradigm for computation, for interaction, for data transmission, or for visual presentation. It is thus not surprising that PVA-related research is distributed over multiple disciplines, motivated by various underlying problems, described in different, sometimes overloaded terms at different levels of detail for different audiences.

show abstract

Section: Discussionmentioning

confidence: 99%

A Review and Characterization of Progressive Visual Analytics

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Zhou et al [33] visually clustered data based on the line-interaction energy computed from the PCP image. Hierarchical clustering methods have also been adopted to help users explore the multilevel clustering information [7,24]. In addition, many filtering-based methods [3,15] can reduce visual clutter caused by too many crossing or overlapping lines in PCPs, while preserving the significant features in the original data.…”

Section: Overview Methodsmentioning

confidence: 99%

Parallel Coordinates with Data Labels

Hong

Ming

et al. 2014

Proceedings of the 7th International Symposium on Visual Information Communication and Interaction

View full text Add to dashboard Cite

Parallel coordinates have been widely used to analyze high-dimensional data. Numerous methods have been designed to provide overview patterns in parallel coordinate plots. However, detailed information is also important in data analysis. When several lines overlap or are close to one another, distinguishing detailed information of polyline crossings is difficult. In this paper, we present a novel approach to address the problem of polyline crossing ambiguity by using data labels. We place different labels along various polylines to give cues for differentiation of lines. We bend the lines and optimize the arrangement of curved lines to provide space for clear visible labels. An energy system that models attractive and repulsive forces of lines is used to guide the search for optimized line arrangement. The experiments on several real datasets demonstrate the effectiveness of our approach.

show abstract

“…There are some techniques proposed in previous research that attempted to enhance the readability of the results by applying clustering techniques or sampling polynies [11] [12] [13] [14]. Moreover the readiness and effectiveness of the parallel coordinates depends on ordering the dimensions and factors, different dimension ordering techniques were presented [15] [16] [17].…”

Section: Background and Related Workmentioning

confidence: 99%

Enhancing Visualization of Multidimensional Data by Ordering Parallel Coordinates Axes

Nabil¹,

Mohamed²,

Kamal³

2019

IJACSA

View full text Add to dashboard Cite

Every year business is overwhelmed by the quantity and variety of data. Visualization of Multi-dimensional data is counter-intuitive using conventional graphs. Parallel coordinates are proposed as an alternative to explore multivariate data more effectively. However, it is difficult to extract relevant information through the parallel coordinates when the data are Multi-dimensional with thousands of lines overlapping. The order of the axes determines the perception of information on parallel coordinates. This paper proposes three new techniques in order to arrange the axes in the most significant relation between the datasets. The datasets used in this paper, for Egyptian patients, with many external factors and medical tests. These factors were collected by a questionnaire sheet, made by medical researchers. The first Technique calculates the correlation between all features and the age of the patient when they get diabetes disease. The second technique is based on merging different features together and arranging the coordinates based on the correlations values. The Third Technique calculates the entropy value for each feature and then arrange the parallel coordinates in descending order based on the positive or negative values. Finally based on the result graphs, we conclude that the second method was more readable and valuable than the other two methods.

show abstract

Progressive parallel coordinates

Cited by 20 publications

References 18 publications

A Review and Characterization of Progressive Visual Analytics

A Review and Characterization of Progressive Visual Analytics

Parallel Coordinates with Data Labels

Enhancing Visualization of Multidimensional Data by Ordering Parallel Coordinates Axes

Contact Info

Product

Resources

About