@phdthesis{oai:doshisha.repo.nii.ac.jp:00001568,
 author = {髙岸, 茉莉子 and Takagishi, Mariko},
 month = {2019-10-11, 2020-04-10},
 note = {本論文では大規模カテゴリカルデータのデータ解釈の場面で生じる問題を考えた．データが大規模な場合，クラスター分析や視覚化などで，データの潜在的な構造を調べる方法が有用とされるが，対象ごとにカテゴリの解釈が異なったり，同じ属性でも回答傾向が異なったりすると解釈が複雑になる．本論文ではそのように既存手法をシンプルに適用するのでは解釈が難しいようなデータに対して，よりわかりやすい解釈をするための手法を開発した．, Large-scale categorical data are often obtained in various fields. As an interpretation of large-scale data tends to be complicated, methods to capture the latent structure in data, such as a cluster analysis and a visualization method are often used to make data more interpretable. However, there are some situations where these methods failed to capture the latent structure that is interpretable (e.g., interpretation of categories by each respondent is different). Therefore in this paper, two problems that often occur in large-scale categorical data analysis is considered, and new methods to address these issues are proposed., application/pdf},
 title = {Clustering and visualization for enhancing interpretation of categorical data},
 year = {},
 yomi = {タカギシ, マリコ}
}