Deciding Entailment of Implications with Support and Confidence in Polynomial Space

TL;DR

This paper explores a logical framework for implications with support and confidence constraints in relational data, proposing a method to decide entailment within polynomial space, bridging data mining concepts with logical reasoning.

Contribution

It introduces a semantic model for implications with support and confidence constraints and provides a decision procedure for entailment in polynomial space.

Findings

Decidability of entailment for constrained implications

Polynomial space complexity of the decision procedure

Bridging data mining rules with logical reasoning

Abstract

Association Rules are a basic concept of data mining. They are, however, not understood as logical objects which can be used for reasoning. The purpose of this paper is to investigate a model based semantic for implications with certain constraints on their support and confidence in relational data, which then resemble association rules, and to present a possibility to decide entailment for them.