• Communications for Statistical Applications and Methods
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• v.15 no.1
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• pp.87-94
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• 2008

Andrews (1972) proposed to combine trigonometric functions to represent n observations of p variates, where the coefficients in linear sums are taken from the values of corresponding observation's respective variates. By viewing Andrews' plot as a collection of n trajectories of p-dimensional objects (observations) as a weighting point loaded with dimensional weights moves along a certain path on the hyper-dimensional sphere, we develop graphical techniques for further uses in data visualization. Specifically, we show that the parallel coordinate plot is a special case of Andrews' plot and we demonstrate the versatility of Andrews' plot with a projection pursuit engine.

• The Korean Journal of Applied Statistics
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• v.21 no.6
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• pp.911-921
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• 2008

Visualization of the gene expression data on a low-dimensional graph is helpful in uncovering biological information contained in the data. In this study, we focus on two modified versions of the parallel coordinate plot. First one is the ePCP(enhanced parallel coordinate plot) which shows "near smooth" connecting curves between axes spaced proportionately to the proximity of re-ordered variables. Second one is APCP(Andrews' type parallel coordinate plot) which is obtained by rotating Andrews' plot that has a form of the parallel coordinate plot. Visualization procdures using ePCP and APCP are given for the lymphoma data case.

• Communications for Statistical Applications and Methods
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• v.15 no.4
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• pp.587-595
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• 2008

Parallel coordinate plot of Inselberg (1985) is useful for visualizing dozens of variables, but so far the plot's applicability is limited to the variables of numerical type. The aim of this study is to extend the parallel coordinate plot so that it can accommodate both numerical and categorical variables. We combine Hayashi's (1950, 1952) quantification method of categorical variables and Hurley's (2004) endlink algorithm of ordering variables for the parallel coordinate plot. In line with our former study (Kwak and Huh, 2008), we develop Andrews' type modification of conventional straight-lines parallel coordinate plot to visualize the mixed-type data.

• Communications for Statistical Applications and Methods
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• v.9 no.2
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• pp.505-514
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• 2002

Multiple correspondence analysis graphically shows the correspondent relationship among categories in multi-way contingency tables. It is well known that the proportions of the principal inertias as part of the total inertia is low in multiple correspondence analysis. Moreover, although this problem can be overcome by using the Benzecri formula, it is not enough to show clear correspondent relationship among categories (Greenacre and Blasius, 1994, Chapter 10). In addition, they show that Andrews' plot is useful in providing the correspondent relationship among categories. However, this method also does not give some concise interpretation among categories when the number of categories is large. Therefore, in this study, we will easily interpret the multiple correspondence analysis by applying the K-means clustering analysis.

• Proceedings of the Korean Statistical Society Conference
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• 2001.11a
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• pp.175-178
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• 2001

다원분할표에서 범주들의 대응관계를 그래프적으로 보여주는 다중대응분석(multiple correspondence analysis)은 주결여성(principal inertia)이 총결여성(total inertia)에서 차지하는 비율이 전반적으로 낮아 설명력(goodness-of-fit)이 낮은 2차원의 대응분석그림을 얻게 된다. 이를 극복하기 위해 Benzecri의 공식을 사용하면 낮은 주결여성을 높이고 새로운 2차원 대응분석그림을 얻을 수 있다. 그러나 이 새로운 대응분석그림도 범주들의 대응관계를 명확히 보여주지는 못한다(Greenacre and Blasius, 1994, chapter 10). 앤드류 플롯(Andrews plot)을 이용하여 범주들의 군집화(clustering)로 다중대응분석을 재해석 하고자 하나 범주의 수가 많은 경우 해석상 어려움이 따른다. 본 소고에서 이와 같은 경우 K-평균 군집분석을 활용하여 다중대응분석의 해석을 용이하게 하고자 한다.