[1]LI Yang,HAO Zhifeng,XIE Guangqiang,et al.Quality-metrics driven multi-dimensional data aggregation and visualization[J].CAAI Transactions on Intelligent Systems,2013,8(4):299-304.[doi:10.3969/j.issn.1673-4785.201304039]
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Quality-metrics driven multi-dimensional data aggregation and visualization

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 8 Number of periods: 2013 4 Page number: 299-304 Column: 学术论文—机器学习 Public date: 2013-08-25
Title:
Quality-metrics driven multi-dimensional data aggregation and visualization
Author(s):
LI Yang1; 2;  HAO Zhifeng2; 3;  XIE Guangqiang1; 2;  YUAN Ganzhao 3
1.School of Automation, Guangdong University of Technology, Guangzhou 510006, China; 2.School of Computers, Guangdong University of Technology, Guangzhou 510006, China; 3.School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China
Keywords:
quality-metrics;  data space;  data aggregation;  K-means;  multi-dimensional data visualization
CLC:
TP391
DOI:
10.3969/j.issn.1673-4785.201304039
Abstract:
For the purpose of this research paper, we examined multi-dimensional data visualization with the quality metrics model; taking data aggregation as a basic means in order to improve the multi-dimensional visualization image quality. Under the quality-metrics driven framework, we put forward a data aggregation algorithm called equipartition K-means++ based on conventional K-means, and thus, were able to improve the algorithm especially as it pertains to data visualization. The aggregated data obtained by equipartition K-means++ may not only preserve most features of the original data, but also improve the image quality after visualization. Our simulation experiments show that at each value of data abstraction level (DAL), equipartition K-means++ get good results, not only in visualization image quality but also quality metrics of histogram difference measure (HDM) and nearest neighbor measure (NNM).
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