First, a distinction whether we are dealing with sparse data (nearly-binary data) or dense data (nearly-continuous) should be made. Second, a distinction whether we are calculating similarity for an unsupervised or a supervised problem should be made. Then, present most common similarity measures and variable selection algorithms for each of four type of problems.

This is a vast area of research. Diving head on might result in serious injury. For example, R statistical package simba (http://cran.r-project.org/web/packages/simba/index.html) lists 56 different similarity/dissimilarity measures just for binary data.