Volume 13 Number 4 (Apr. 2018)
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JCP 2018 Vol.13(4): 407-416 ISSN: 1796-203X
doi: 10.17706/jcp.13.4.407-416

Quantitative Approach on Parallel Coordinates and Scatter Plots for Multidimensional-Data Visual Analytics

Phi Giang Pham1,2, Mao Lin Huang1
1University of Technology, Sydney, NSW, Australia.
2Cantho Technical Economic College, Cantho, Viet Nam.


Abstract—Parallel coordinates and scatter plots are two well-known visualization techniques for multidimensional data analytics and often employed cooperatively for flexibility increase in exploration of such data. Existing approaches approximately consider qualitative issues and single attribute comparison, which might face statistic challenges in case of quantitative requirement. This paper introduces a new quantitative approach for visual enhancement of parallel coordinates and scatter plots in term of multiple attribute comparison. The method is based on the visual integration of interactive stacked bars and visual queries on parallel axes and scatter charts. The parallel coordinates play the role of a context view while the scatter charts are for focus details. Using the technique, users could not only quantitatively analyze multivariate data, but also flexibly compare multiple target attributes. Moreover, further investigation is enabled for deep understanding of desired information. The characteristics and usefulness of our approach are demonstrated via a case study with two typical use cases.

Index Terms—Multidimensional data, visual analysis, parallel coordinates, scatter plots, visual query.

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Cite: Phi Giang Pham, Mao Lin Huang, "Quantitative Approach on Parallel Coordinates and Scatter Plots for Multidimensional-Data Visual Analytics," Journal of Computers vol. 13, no. 4, pp. 407-416, 2018.

General Information

ISSN: 1796-203X
Frequency: Monthly (2006-2014); Bimonthly (Since 2015)
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,etc
E-mail: jcp@iap.org
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