Association Rules: An Axiomatic Approach

Author : Gabrielle Gayer, Stefania Minardi, Fan Wang, Itzhak Gilboa

Abstract :We consider a reasoner who generates predictions using association rules, each of which can be viewed as a conditional statement regarding observed binary variables x, and making a prediction about another binary variable, y. Rules provide support to their predictions, which is aggregated in an additive way. The weight of each rule depends on the database of observations, and is aggregated over all observations in which the rule applied. We provide axioms on a reasoner, who makes predictions given databases of observations, who can be modeled as following this rule-based prediction. Generalizations and applications are discussed.

Keywords :Rule-based reasoning, association rules, predictive modeling, binary variables, conditional statements, data-driven predictions, additive aggregation, weighted rules, observational databases, axiomatic reasoning, artificial intelligence, decision support, machine learning, automated reasoning, knowledge-based systems.

Conference Name :International Conference on Cognitive Psychology and Memory (ICCPM-25)

Conference Place Washington DC, USA

Conference Date 25th Jan 2025