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행렬도에서 군집분석의 활용

Applications of Cluster Analysis in Biplots

  • 발행 : 2008.01.31

초록

행렬도 (biplot)는 이원표 자료행렬 (two-way data matrix)의 행과 열을 그래프에 동시에 나타내어 이들의 관계를 살피려는 다변량 그래프적 분석기법이다 (Gower와 Hand, 1996; 최용석, 2006, 1장). 그래프적 분석기법은 그 특성상 대용량 자료를 해석하는 데는 어려움이 따른다. 따라서, 자료를 효과적으로 줄일 수 있는 군집분석을 활용하여 원자료와 변수간의 행렬도가 아닌 각 군집과 변수간의 행렬도 분석을 수행함으로써, 기존의 행렬도에서 해석의 어려웠던 대용량 자료에 대한 해석이 가능하게 되며, 자료에 대한 정보를 쉽게 파악할 수 있는 장점을 가진다.

Biplots are the multivariate analogue of scatter plots. They approximate the multivariate distribution of a sample in a few dimensions, typically two, and they superimpose on this display representations of the variables on which the samples are measured(Gower and Hand, 1996, Chapter 1). And the relationships between the observations and variables can be easily seen. Thus, biplots are useful for giving a graphical description of the data. However, this method does not give some concise interpretations between variables and observations when the number of observations are large. Therefore, in this study, we will suggest to interpret the biplot analysis by applying the K-means clustering analysis. It shows that the relationships between the clusters and variables can be easily interpreted. So, this method is more useful for giving a graphical description of the data than using raw data.

키워드

참고문헌

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  7. Gabriel, K. R. (1981). Biplot display of multivariate matrices for inspection of data and diagnosis. In Interpreting Multivariate Data (Barnett, V., ed), 147-173, Wiley, New York
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