@techreport{oai:ipsj.ixsq.nii.ac.jp:00239029,
 author = {白田, 由香利 and バサビ, チャクラボルティ and Yukari, Shirota and Basabi Chakrabory},
 issue = {40},
 month = {Sep},
 note = {現在の統計学では，多変量解析の殆どの手法は共分散あるいは相関係数に基づいて構築されている．共分散及び相関係数は 2 変数間の関係のみを扱うものであり，多数の変数間の関係性を一度に計算するものではない．本稿では，3 変数の間の関係性を一度に示すトリプル相関係数を提案する．評価として，階層型クラスタリングによって得られたクラスターの類似度とトリプル相関係数の比較を行う．, In contemporary statistical analysis, most multivariate techniques are founded on covariance or correlation coefficients. These coefficients, however, are limited to capturing the relationship between pairs of variables and do not extend to the simultaneous analysis of multiple variables. This paper introduces the concept of the triple correlation coefficient, designed to elucidate the relationships among three variables concurrently. We will compare the similarity of clusters obtained through hierarchical clustering with the triple correlation coefficient for evaluation.},
 title = {トリプル相関係数の提案},
 year = {2024}
}