期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2019
卷号：10
期号：9
页码：340-344
出版社：Science and Information Society (SAI)
摘要：Every year business is overwhelmed by the
quantity and variety of data. Visualization of Multi-dimensional
data is counter-intuitive using conventional graphs. Parallel
coordinates are proposed as an alternative to explore
multivariate data more effectively. However, it is difficult to
extract relevant information through the parallel coordinates
when the data are Multi-dimensional with thousands of lines
overlapping. The order of the axes determines the perception of
information on parallel coordinates. This paper proposes three
new techniques in order to arrange the axes in the most
significant relation between the datasets. The datasets used in
this paper, for Egyptian patients, with many external factors and
medical tests. These factors were collected by a questionnaire
sheet, made by medical researchers. The first Technique
calculates the correlation between all features and the age of the
patient when they get diabetes disease. The second technique is
based on merging different features together and arranging the
coordinates based on the correlations values. The Third
Technique calculates the entropy value for each feature and then
arrange the parallel coordinates in descending order based on
the positive or negative values. Finally based on the result
graphs, we conclude that the second method was more readable
and valuable than the other two methods.
关键词：Parallel coordinates; visualization; correlation
coefficient; entropy function
