Proceedings of the twenty-seventh International Conference of the Florida Artificial Intelligence Research Society (FLAIRS) : May 21 - 23, 2014 Pensacola Beach, Florida, USA
Learning rules is a common way of extracting useful information
from knowledge or data bases. Many of such data sets
contain numerical attributes. However, approaches like ILP
or association rule mining are optimized for data with categorical values, and considering numerical attributes is expensive. In this paper, we present an extension to top-down
ILP algorithms such as FOIL, which enables an efficient discovery
of rules from data with both numerical and categorical
attributes. Our approach comprises a preprocessing phase
for computing the correlations between numerical and categorical attributes, as well as an extension to the ILP refinement step, which enables us to detect interesting candidate rules and to suggest refinements with relevant attribute combinations. We report on experiments with U.S. Census data, Freebase and DBpedia, and show that our approach helps to efficiently discover rules with numerical intervals.
Dieser Eintrag ist Teil der Universitätsbibliographie.
Search Authors in