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Visual Analytics of Large Multi-Dimensional Data Using Variable Binned Scatter Plots

Visual Analytics of Large Multi-Dimensional Data Using Variable Binned Scatter Plots

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HAO, Ming C., Umeshwar DAYAL, Ratnesh K. SHARMA, Daniel A. KEIM, Halldór JANETZKO, 2010. Visual Analytics of Large Multi-Dimensional Data Using Variable Binned Scatter Plots. IS&T/SPIE Electronic Imaging. San Jose, California. In: PARK, Jinah, ed., Ming C. HAO, ed., Pak C. WONG, ed., Chaomei CHEN, ed.. Visualization and Data Analysis 2010. IS&T/SPIE Electronic Imaging. San Jose, California. SPIE, pp. 753006. Available under: doi: 10.1117/12.840142

@inproceedings{Hao2010-01-17Visua-6310, title={Visual Analytics of Large Multi-Dimensional Data Using Variable Binned Scatter Plots}, year={2010}, doi={10.1117/12.840142}, number={7530}, publisher={SPIE}, series={SPIE Proceedings}, booktitle={Visualization and Data Analysis 2010}, editor={Park, Jinah and Hao, Ming C. and Wong, Pak C. and Chen, Chaomei}, author={Hao, Ming C. and Dayal, Umeshwar and Sharma, Ratnesh K. and Keim, Daniel A. and Janetzko, Halldór} }

Sharma, Ratnesh K. Janetzko, Halldór Hao, Ming C. Visual Analytics of Large Multi-Dimensional Data Using Variable Binned Scatter Plots Dayal, Umeshwar Keim, Daniel A. Also publ. in: Visualization and data analysis 2010 : 18 - 19 January 2010, San Jose, California, United States ; proceedings IS&T/SPIE electronic imaging, science and technology / Jinah Park ... (Ed.). - Bellingham, Wash. : SPIE [u.a.], 2010. - Artikel 06. - ISBN 978-0-8194-7923-5 application/pdf Keim, Daniel A. Hao, Ming C. deposit-license Sharma, Ratnesh K. The scatter plot is a well-known method of visualizing pairs of two-dimensional continuous variables. Multidimensional data can be depicted in a scatter plot matrix. They are intuitive and easy-to-use, but often have a high degree of overlap which may occlude a significant portion of data. In this paper, we propose variable binned scatter plots to allow the visualization of large amounts of data without overlapping. The basic idea is to use a non-uniform (variable) binning of the x and y dimensions and plots all the data points that fall within each bin into corresponding squares. Further, we map a third attribute to color for visualizing clusters. Analysts are able to interact with individual data points for record level information. We have applied these techniques to solve real-world problems on credit card fraud and data center energy consumption to visualize their data distribution and cause-effect among multiple attributes. A comparison of our methods with two recent well-known variants of scatter plots is included. 2010-01-17 2011-03-24T16:11:22Z Janetzko, Halldór eng Dayal, Umeshwar 2011-03-24T16:11:22Z

Dateiabrufe seit 01.10.2014 (Informationen über die Zugriffsstatistik)

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