• Title, Summary, Keyword: 합의행렬그림

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The Similarity Plot for Comparing Clustering Methods (군집분석 방법들을 비교하기 위한 상사그림)

  • Jang, Dae-Heung
    • The Korean Journal of Applied Statistics
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    • v.26 no.2
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    • pp.361-373
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    • 2013
  • There are a wide variety of clustering algorithms; subsequently, we need a measure of similarity between two clustering methods. Such a measure can compare how well different clustering algorithms perform on a set of data. More numbers of compared clustering algorithms allow for more number of valuers for a measure of similarity between two clustering methods. Thus, we need a simple tool that presents the many values of a measure of similarity to compare many clustering methods. We suggest some graphical tools to compareg many clustering methods.

INFLUENCE FUNCTIONS IN MULTIPLE CORRESPONDENCE ANALYSIS (다중 대응 분석에서의 영향 함수)

  • Hong Gie Kim
    • The Korean Journal of Applied Statistics
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    • v.7 no.1
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    • pp.69-74
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    • 1994
  • Kim (1992) derived influence functions of rows and columns on the eigenvalues obtained in correspondence analysis (CA) of two-way contingency tables. As in principal component analysis, the eigenvalues are of great importance in CA. The goodness of a two dimensional correspondence plot is determined by the ratio of the sum of the two largest eigenvalues to the sum of all the eigenvalues. By investigating those rows and columns with high influence, a correspondence plot may be improved. In this paper, we extend the influence functions of CA to multiple correspondence analysis (MCA), which is a CA of multi-way contigency tables. An explicit formula of the influence function is given.

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