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SCIENCE CHINA Information Sciences, Volume 63 , Issue 8 : 184101(2020) https://doi.org/10.1007/s11432-019-2647-3

Ordered matrix representation supporting the visual analysis of associated data

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  • ReceivedJun 14, 2019
  • AcceptedSep 9, 2019
  • PublishedMar 26, 2020

Abstract

There is no abstract available for this article.


Acknowledgment

This work was supported by National Key RD Program of China (Grant No. 2018YFC1603602), National Natural Science Foundation of China (Grant Nos. 61972010, 61772456, 61761136020), and Basic Research Project of the Ministry of Science and Technology (Grant No. 2015FY111200).


Supplement

Videos and other supplemental documents.


References

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[3] Page L, Brin S, Motwani R, et al. The PageRank Citation Ranking: Bringing Order to the Web. Technical Report 1999-66, Stanford InfoLab. Google Scholar

[4] Mei H H, Ma Y X, Wei Y T. The design space of construction tools for information visualization: A survey. J Visual Languages Computing, 2018, 44: 120-132 CrossRef Google Scholar

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  • Figure 1

    (Color online) Screenshot of the Rank-Vis system for visually analysing the pesticide-residue dataset selected in the parameter panel (E). (${\rm~A}_1$) The row and column vectors in matrix $A$ are ranked in alphabetical order. (${\rm~A}_2$) Matrix $B$ is the same matrix with the row and column vectors reordered by the proposed RW-Rank algorithm. (${\rm~B}_1$) and (${\rm~B}_2$) are the word clouds, in which the sizes of the pesticide and agricultural product names are mapped to the detection and pesticide residue detection frequencies, respectively. (C) Pie chart showing the percentages of four residue levels (levels 1, 2, 3 and undetermined) in different agricultural products selected by clicking their names in (${\rm~B}_1$). (D) Parallel coordinate shows the detailed information of a specific agricultural product selected by clicking its name in (${\rm~B}_2$).