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I have 17 numeric and 5 binary (0-1) variables, with 73 samples in my dataset. I need to run a cluster analysis. I know that the Gower distance is a good metric for datasets with mixed variables. However, I couldn't understand how the Gower distance calculates the difference between binary variables. It seems to me that it is not different from Euclidean distance.

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  • $\begingroup$ Your question isn't quite clear. Are you simply asking 'how does the Gower distance calculate the difference between binary variables'? What does "there is no difference than Euclidean" mean? $\endgroup$ Oct 21, 2014 at 15:42
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    $\begingroup$ Thank you. Sorry, I ask how Gower calculate the difference between binary variables. I mean, I couldn't understand the differences btw. Euclidean and Gower for binary variable. $\endgroup$ Oct 21, 2014 at 15:58
  • $\begingroup$ Have you searched this site for Gower? stats.stackexchange.com/a/15313/3277 $\endgroup$
    – ttnphns
    Oct 21, 2014 at 16:48
  • $\begingroup$ Yes I did. Euclidean distance is 0, if both samples have same value, 1 if not. What about Gower? $\endgroup$ Oct 21, 2014 at 16:57
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    $\begingroup$ @EmrahBilgiç, Gower metric is similarity, not distance. It becomes "distance" when is subtracted from 1. Read under the link above how it processes binary data. $\endgroup$
    – ttnphns
    Oct 21, 2014 at 17:31

2 Answers 2

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How about binary attributes that have the values "m" and "f", for "male" and "female"?

You do realize that for a dicotomous variable all you can get out is "same" or "different"? The key point difference between distances is not if the value is 1 or 0; but how multiple variables are combined.

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Gower distance uses Manhattan for calculating distance between continuous datapoints and Dice for calculating distance between categorical datapoints

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