Stroke data analysis through a HVN visual mining platform

Huang, ML; Yue, Z; Nguyen, QV; Liang, J; Luo, Z

Stroke data analysis through a HVN visual mining platform

Huang, ML

Yue, Z

Nguyen, QV Liang, J

Luo, Z

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2019 23rd International Conference in Information Visualization - Part II, IV-2 2019, 2019, pp. 1 - 6
Issue Date:: 2019-07-01

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	Stroke Data Analysis through a HVN Visual Mining Platform v2.pptx	Accepted Manuscript version	2.71 MB	Microsoft Powerpoint XML	View/Open

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Field	Value	Language
dc.contributor.author	Huang, ML https://orcid.org/0000-0002-6896-6480	en_US
dc.contributor.author	Yue, Z https://orcid.org/0000-0001-6269-5950	en_US
dc.contributor.author	Nguyen, QV	en_US
dc.contributor.author	Liang, J https://orcid.org/0000-0001-7179-5208	en_US
dc.contributor.author	Luo, Z	en_US
dc.date.issued	2019-07-01	en_US
dc.identifier.citation	Proceedings - 2019 23rd International Conference in Information Visualization - Part II, IV-2 2019, 2019, pp. 1 - 6	en_US
dc.identifier.isbn	9781728128504	en_US
dc.identifier.uri	http://hdl.handle.net/10453/134614
dc.description.abstract	© 2019 IEEE. Today there are abounding collected data in cases of various diseases in medical sciences. Physicians can access new findings about diseases and procedures in dealing with them by probing these data. Clinical data is a collection of large and complex datasets that commonly appear in multidimensional data formats. It has been recognized as a big challenge in modern data analysis tasks. Therefore, there is an urgent need to find new and effective techniques to deal with such huge datasets. This paper presents an application of a new visual data mining platform for visual analysis of the stroke data for predicting the levels of risk to those people who have the similar characteristics of the stroke patients. The visualization platform uses a hierarchical clustering algorithm to aggregate the data and map coherent groups of data-points to the same visual elements-curved 'super-polylines' that significantly reduces the visual complexity of the visualization. On the other hand, to enable users to interactively manipulate data items (super-polylines) in the parallel coordinates geometry through the mouse rollover and clicking, we created many 'virtual nodes' along the multi-axis of the visualization based on the hierarchical structure of the value range of selected data attributes. The experimental result shows that we can easily verify research hypothesis and reach to the conclusion of research questions through human-data & human-algorithm interactions by using this visual platform with a fully transparency manner of data processing.	en_US
dc.relation.ispartof	Proceedings - 2019 23rd International Conference in Information Visualization - Part II, IV-2 2019	en_US
dc.relation.isbasedon	10.1109/IV-2.2019.00010	en_US
dc.title	Stroke data analysis through a HVN visual mining platform	en_US
dc.type	Conference Proceeding
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2019 IEEE. Today there are abounding collected data in cases of various diseases in medical sciences. Physicians can access new findings about diseases and procedures in dealing with them by probing these data. Clinical data is a collection of large and complex datasets that commonly appear in multidimensional data formats. It has been recognized as a big challenge in modern data analysis tasks. Therefore, there is an urgent need to find new and effective techniques to deal with such huge datasets. This paper presents an application of a new visual data mining platform for visual analysis of the stroke data for predicting the levels of risk to those people who have the similar characteristics of the stroke patients. The visualization platform uses a hierarchical clustering algorithm to aggregate the data and map coherent groups of data-points to the same visual elements-curved 'super-polylines' that significantly reduces the visual complexity of the visualization. On the other hand, to enable users to interactively manipulate data items (super-polylines) in the parallel coordinates geometry through the mouse rollover and clicking, we created many 'virtual nodes' along the multi-axis of the visualization based on the hierarchical structure of the value range of selected data attributes. The experimental result shows that we can easily verify research hypothesis and reach to the conclusion of research questions through human-data & human-algorithm interactions by using this visual platform with a fully transparency manner of data processing.

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http://hdl.handle.net/10453/134614