Information-Theoretical Mining of Determining Sets for Partially Defined Functions

This paper describes an algorithm that determines the minimal sets of variables that determine the values of a discrete partial function. The algorithm is based on the notion of entropy of a partition and is able to achieve an optimal solution. A limiting factor is introduced to restrict the search, thereby providing the option to reduce running time. Experimental results are provided that demonstrate the efficiency of the algorithm for functions with up to 24 variables. The effect of the limiting factor on the optimality of the algorithm for different sizes of partial functions is also examined.