I have a data set with about 50K rows and 100 columns. You can consider every row to be representing one restaurant.
My goal is to calculate dissimilarities between all the restaurants - Gower's coefficient.
Of those 100 columns (features), a few of them are numeric data and nominal data. The problem is the other columns (about 90) are very sparse binary data (1/0).
I do think that those 90 columns of binary data can be reduced to some smaller number of columns, so that the computational time can be reduced significantly. But I don't know what method I should use to reduce such a large amount of binary data.
Can anyone give me some suggestions?
It will be most helpful if you can provide me some references and R code.