Journal
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
Volume 31, Issue 3, Pages 790-801Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/10618600.2022.2035737
Keywords
Clustering; Contingency table; External information; Multiple correspondence analysis; Visualization
Categories
Funding
- Japan Society for the Promotion of Science KAKENHI [20K19755]
- Grants-in-Aid for Scientific Research [20K19755] Funding Source: KAKEN
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In multiple correspondence analysis, a biplot can be used to depict the relationships between categories and individuals. Additional information about individuals can enhance interpretation capacities, such as including class information to facilitate the interpretation of relationships between individuals and categories.
In multiple correspondence analysis, both individuals (observations) and categories can be represented in a biplot that jointly depicts the relationships across categories and individuals, as well as the associations between them. Additional information about the individuals can enhance interpretation capacities, such as by including class information for which the interdependencies are not of immediate concern, but that facilitate the interpretation of the plot with respect to relationships between individuals and categories. This article proposes a new method which we call multiple-class cluster correspondence analysis that identifies dusters specific to classes. The proposed method can construct a biplot that depicts heterogeneous tendencies of individual members, as well as their relationships with the original categorical variables. A simulation study to investigate the performance of the proposed method and an application to data regarding road accidents in the United Kingdom confirms the viability of this approach. Supplementary materials for this article are available online.
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