Bigfoot Climbing The Hill With Ilp To Grow Patterns In Fuzzy Tensors

Fuzzy tensors encode to what extent n-ary predicates are satisfied. The disjunctive box cluster model is a regression model where sub-tensors are explanatory variables for the values in the fuzzy tensor. In this article, the most informative patterns according to that model, with high areas times squared densities, are grown by hill-climbing from fragments of them, that a complete algorithm provides. At every iteration, an optimization problem (or its linear relaxation) is solved thanks to integer linear programming (or greedily). A forward selection then chooses among the discovered patterns a non-redundant subset that fits, but does not overfit, the tensor. Experiments show the proposal discovers high-quality patterns and outperforms state-of-the-art approaches when applied to 0/1 tensors, a special case.