Best-case exhaustive feature analysis of the food patterns datamatrix.

Exhaustive multivariate feature analysis is used to highlight pairs of FP features giving similarity value equal to 1. Features in column 1 give similarity equal to 1 if and only if coupled with one of the features in column 2. For example, feature cereals (i = 3) gives a similarity that is always equal to 1 alone or together with any set of other features. Relevance of individual features in column 1 is based on the number of other features in column 2 with which it can yield unitary value after pairing. The most relevant features are cereals, milk, and meat.