Journal of Korean Society for Quality Management (품질경영학회지)

Korean Society for Quality Management (한국품질경영학회)
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Categorical Data Clustering Analysis Using Association-based Dissimilarity

연관성 기반 비유사성을 활용한 범주형 자료 군집분석

  • Lee, Changki (College of Business Administration, Dongguk University) ;
  • Jung, Uk (College of Business Administration, Dongguk University)
  • 이창기 (동국대학교 경영대학) ;
  • 정욱 (동국대학교 경영대학)
  • Received : 2019.03.10
  • Accepted : 2019.03.25
  • Published : 2019.06.30
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Abstract

Purpose: The purpose of this study is to suggest a more efficient distance measure taking into account the relationship between categorical variables for categorical data cluster analysis. Methods: In this study, the association-based dissimilarity was employed to calculate the distance between two categorical data observations and the distance obtained from the association-based dissimilarity was applied to the PAM cluster algorithms to verify its effectiveness. The strength of association between two different categorical variables can be calculated using a mixture of dissimilarities between the conditional probability distributions of other categorical variables, given these two categorical values. In particular, this method is suitable for datasets whose categorical variables are highly correlated. Results: The simulation results using several real life data showed that the proposed distance which considered relationships among the categorical variables generally yielded better clustering performance than the Hamming distance. In addition, as the number of correlated variables was increasing, the difference in the performance of the two clustering methods based on different distance measures became statistically more significant. Conclusion: This study revealed that the adoption of the relationship between categorical variables using our proposed method positively affected the results of cluster analysis.

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References

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