Detecting patterns of change using enhanced parallel coordinates visualization

Zhao, Kai; Liu, B.; Tirpak, Thomas M.; Schaller, Andreas

doi:10.1109/icdm.2003.1251023

Cited by 12 publications

(9 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…General techniques for interacting with parallel coordinates have been discussed by Siirtola (2000aSiirtola ( , 2003, Graham and Kennedy (2003), Johansson et al (2004), Zhao et al (2003), Hauser et al (2002a,b), Hauser and Kosara (2004) and Novotny (2004).…”

Section: Related Workmentioning

confidence: 98%

Interacting with parallel coordinates

Siirtola

Räihä

2006

Interacting with Computers

View full text Add to dashboard Cite

Parallel coordinate visualizations have a reputation of being difficult to understand, expertonly representations. We argue that this reputation may be partially unfounded, because many of the parallel coordinate browser implementations lack essential features. This paper presents a survey of current interaction techniques for parallel coordinate browsers and compares them to the visualization design guidelines in the literature. In addition, we report our experiences with parallel coordinate browser prototypes, and describe an experiment where we studied the immediate usability of parallel coordinate visualizations. In the experiment, 16 database professionals performed a set of tasks both with the SQL query language and a parallel coordinate browser. The results show that although the subjects had doubts about the general usefulness of the parallel coordinate technique, they could perform the tasks more efficiently with a parallel coordinate browser than with their familiar query language interface.

show abstract

Section: Related Workmentioning

confidence: 98%

Interacting with parallel coordinates

Siirtola

Räihä

2006

Interacting with Computers

View full text Add to dashboard Cite

show abstract

“…Among many visualization techniques for multidimensional data [33], parallel coordinates is one of the most elegant yet simple tools, and we select it as the visualization platform for our proposed algorithms. Parallel coordinates, which transforms multivariate data into 2D polylines (or 'lines' for short), has been widely used in many information visualization applications [18,28] as well as data mining [35]. Figure 1(a) is an example of visualizing 4-dimensional data by parallel coordinates.…”

Section: Introductionmentioning

confidence: 99%

Visualizing Clusters in Parallel Coordinates for Visual Knowledge Discovery

Xiang

Fuhry

Jin

et al. 2012

Advances in Knowledge Discovery and Data Mining

View full text Add to dashboard Cite

Abstract. Parallel coordinates is frequently used to visualize multidimensional data. In this paper, we are interested in how to effectively visualize clusters of multi-dimensional data in parallel coordinates for the purpose of facilitating knowledge discovery. In particular, we would like to efficiently find a good order of coordinates for different emphases on visual knowledge discovery. To solve this problem, we link it to the metric-space Hamiltonian path problem by defining the cost between every pair of coordinates as the number of inter-cluster or intra-cluster crossings. This definition connects to various efficient solutions and leads to very fast algorithms. In addition, to better observe cluster interactions, we also propose to shape clusters smoothly by an energy reduction model which provides both macro and micro view of clusters.

show abstract

“…Zhao et al proposed a technique of rearranging variables to better identify patterns of interest. 7 This work contained a query tool that enabled the user to describe a specific target pattern to be displayed. Johansson et al introduced a method to simultaneously examine the relationship of a single dimension to many dimensions.…”

Section: Related Workmentioning

confidence: 99%

“…This allows researchers to eliminate redundant input parameters for more efficient use of modeling and simulation computational resources. For example, should two parameters exhibit a strong correlation, one might be held constant while the other is varied in order to capture all of the unique Received June 30, 2015; accepted for publication Nov. 16,2015; published online Jan. 7,2016. Associate Editor: David Kao.…”

Section: Introductionmentioning

confidence: 99%

Parameter Space Visualization for Large-scale Datasets Using Parallel Coordinate Plots

Glendenning¹,

Wischgoll²,

Harris³

et al. 2016

jist

View full text Add to dashboard Cite

Abstract. Visualization is an important task in data analytics, as it allows researchers to view patterns within the data instead of reading through extensive raw data. Allowing the ability to interact with the visualizations is an essential aspect, since it provides the ability to intuitively explore data to find meaning and patterns more efficiently. Interactivity, however, becomes progressively more difficult as the size of the dataset increases. This project begins by leveraging existing web-based data visualization technologies, and extends their functionality through the use of parallel processing. This methodology utilizes state-of-the-art techniques, such as Node.js, to split the visualization rendering and user interactivity controls between a client-server infrastructure without having to rebuild the visualization technologies. The approach minimizes data transfer by performing the rendering step on the server while allowing for the use of high-performance computing systems to render the visualizations more quickly. In order to improve the scaling of the system with larger datasets, parallel processing and visualization optimization techniques are used. This work uses parameter space data generated from mindmodeling.org to showcase the authors' methodology for handling large-scale datasets while retaining interactivity and user friendliness. INTRODUCTIONThe ability to make rapid visual assessments of parameter spaces has the potential to change the workflow for both model simulation and model fitting/parameter recovery. It enables the rapid identification of input parameters that result in similar output data or model behaviors. This allows researchers to eliminate redundant input parameters for more efficient use of modeling and simulation computational resources. For example, should two parameters exhibit a strong correlation, one might be held constant while the other is varied in order to capture all of the unique

show abstract

Detecting patterns of change using enhanced parallel coordinates visualization

Cited by 12 publications

References 5 publications

Interacting with parallel coordinates

Interacting with parallel coordinates

Visualizing Clusters in Parallel Coordinates for Visual Knowledge Discovery

Parameter Space Visualization for Large-scale Datasets Using Parallel Coordinate Plots

Contact Info

Product

Resources

About