On minimal sets of graded attribute implications
Artificial Intelligence
2014-12-09 v2
Abstract
We explore the structure of non-redundant and minimal sets consisting of graded if-then rules. The rules serve as graded attribute implications in object-attribute incidence data and as similarity-based functional dependencies in a similarity-based generalization of the relational model of data. Based on our observations, we derive a polynomial-time algorithm which transforms a given finite set of rules into an equivalent one which has the least size in terms of the number of rules.
Cite
@article{arxiv.1405.7076,
title = {On minimal sets of graded attribute implications},
author = {Vilem Vychodil},
journal= {arXiv preprint arXiv:1405.7076},
year = {2014}
}