I have different datasets and I want to find out the features that are similar among the datasets. The datasets are of varying sizes. example: dataset1 has columns a,b,c,d,e dataset2 has columns m,n,o,p,q. We as human some how see that column a similar to m , c is similar to n and e is similar to q. But how to achieve this from ML?
To find clusters of features, you'll have to define a distance metric. A distance metric is a numeric value of how close together the values of the data are. Distance metrics depend on how the data is measured. Common ways to measure data are binary, nominal, and interval/ratio. Distance metrics can include distributions, Kullback–Libeler (KL) divergence and earth mover's distance.