TY - GEN
T1 - Information-theoretical mining of determining sets for partially defined functions
AU - Simovici, Dan A.
AU - Pletea, Dan
AU - Vetro, Rosanne
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/77955316159
UR - https://www.scopus.com/pages/publications/77955316159#tab=citedBy
U2 - 10.1109/ISMVL.2010.61
DO - 10.1109/ISMVL.2010.61
M3 - Conference contribution
AN - SCOPUS:77955316159
SN - 9780769540245
T3 - Proceedings of The International Symposium on Multiple-Valued Logic
SP - 294
EP - 299
BT - ISMVL 2010 - 40th IEEE International Symposium on Multiple-Valued Logic
T2 - 40th IEEE International Symposium on Multiple-Valued Logic, ISMVL 2010
Y2 - 26 May 2010 through 28 May 2010
ER -