Rescaling a Proximity Matrix Using Entropy in Brand-switching Data

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  • 銘柄変更データにおけるエントロピーを用いた親近度行列の再構成法
  • メイガラ ヘンコウ データ ニ オケル エントロピー オ モチイタ シンキンド ギョウレツ ノ サイコウセイホウ

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Several researchers have analyzed the brand-switching matrix using multidimensional scaling models. When a brand-switching matrix is analyzed using multidimensional scaling, it is necessary to rescale the matrix because there are differences among the overall sums of the rows and columns that depend on external factors, such as market share. This study introduces a new method that rescales one-mode two-way proximity matrices using entropy which shows the variance of brand-switching. In the present paper, we calculate the entropy of a row and column of a car-switching data, and use the result to reconstruct a new matrix. Then we analyze this matrix using multidimensional scaling. The results suggest that the congurations rescaled using our method can be interpreted as “circumplex”.

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