# How Gower's dissimilarity handle missing values in numeric columns?

I would like to ask a question about Gower dissimilarity, I was wondering how Gower measure handle missing values in numeric columns, especially that Gower standardized each column based on the range of the same attribute ?

I have read both details of functions daisy and gower.dist in R and their original source (chapter 1 of Kaufman and Rousseeuw (1990)) but I got confuse. http://www.inside-r.org/packages/cran/StatMatch/docs/gower.dist https://stat.ethz.ch/R-manual/R-devel/library/cluster/html/daisy.html

I tried also to look at similar discussions in this website Gower distance and MDS: How to determine which variables count? but I did not find an answer.

also imputing the data with a dummy/mean values are not an option for me, I need my data as it is. my data is students' exams marks.

• The ideas behind Gower similarity are themselves unrelated to the question of missing values. It is left on function and it options. Normally, pairwise treatment of missings should be allowed. In this regime, all cases are taken and two cases are compared (produce the similarity) if there is at least one variable where both are valid values. Additionally, for binary variables, the two cases are compared only if at least one of the two has "1" value in the binary variables. Commented Jun 8, 2016 at 12:34
• (cont.) In listwise regime, cases are excluded whenever they have at least one missing datum. You should consult with the documentation of your function to know how it is there. Commented Jun 8, 2016 at 12:35

It's your choice. There is no "correct" way.

The most "correct" way would be the work with two similarities. An upper bound and a lower bound.

Consider this toy example:

dist(  [A, B],  [C,?] )


if the missing value is D then you get a similarity of 0, that is your worst case. But if the missing value is B, and say you don't have any other records with a B and no A either, then it even could be the most similar object.

But then you would need algorithms that can handle this well, and I don't know of any.

A popular approach is missing value imputation. By replacing missing values (at least temporarily) with your best estimate, you are often closest to the real result.

Another popular approach is to ignore records with missing data.