For a K-mean cluster analysis, when variables are percentages have we to use n-1 variables?

07 December 2022 1 4K Report

Good morning,

I would like to share this question.

I am conducting a K-mean clustering analysis. My aim is to investigate if I can observe some patterns of food consumption. For my dataset, each row is the percentage of selection for three categories of food (CAT1 CAT2 CAT3) for one person. Thus, these three variables are associated: from two variables, we can find the percentage of the third by a simple formula (%CAT3 = 100 - (%CAT1+%CAT2). As K-mean clustering is sensitive to multicollinearity, do you think that it is better to run the K-mean algorithm with two variables only?

Thanks for your attention,

Ines

Aki Koivu

Yes, you should use k-means clustering with the variables CAT1 and CAT2 only. Since CAT3 is a computational variable linearly calculated from CAT1 and CAT2, it provides no additional information, other than imposing unnecessary collinearity.

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