CAJ | 학술논문

以多维数据可视化为研究对象，在质量度量模型下，采用数据聚合为基本手段，来提高多维数据可视化的图像质量。在质量度量指标驱动的框架下提出了均分K-means＋＋数据聚合算法，在传统K-means算法的基础上，专门以数据可视化为目的对算法进行了改进，使得算法聚合得到的数据既能够较好地保持原数据的大部分特性，又能显著地提高可视化后的图像质量。仿真实验证明，在不同的数据抽象级别DAL下，无论是图像质量指标还是质量度量指标HDM（直方图差值度量）、NNM（最近邻距离度量），算法都表现出了较好的仿真结果。
이다유수거가시화위연구대상，재질량도량모형하，채용수거취합위기본수단，래제고다유수거가시화적도상질량。재질량도량지표구동적광가하제출료균분K-means＋＋수거취합산법，재전통K-means산법적기출상，전문이수거가시화위목적대산법진행료개진，사득산법취합득도적수거기능구교호지보지원수거적대부분특성，우능현저지제고가시화후적도상질량。방진실험증명，재불동적수거추상급별DAL하，무론시도상질량지표환시질량도량지표HDM（직방도차치도량）、NNM（최근린거리도량），산법도표현출료교호적방진결과。
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 im-age quality. Under the quality-metrics driven framework, we put forward a data aggregation algorithm called equi-partition 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 visualiza-tion image quality but also quality metrics of histogram difference measure ( HDM ) and nearest neighbor measure ( NNM) .