Approximate Denial Constraints

Understand

The problem of mining integrity constraints from data has been extensively studied over the past two decades for commonly used types of constraints including the classic Functional Dependencies (FDs) and the more general Denial Constraints (DCs).

• In this paper, we investigate the problem of mining approximate DCs (i.e., DCs that are "almost" satisfied) from data.
• Considering approximate constraints allows us to discover more accurate constraints in inconsistent databases, detect rules that are generally correct but may have a few exceptions, as well as avoid overfitting and obtain more general and less contrived constraints.
• We introduce the algorithm ADCMiner for mining approximate DCs.