On Temporal Validity Analysis of Association Rules

Association rule mining [1] is a prominent data-mining method used in many domains. Despite the fact that most large datasets are collected over longer time spans, the considered systems are in most cases assumed stationary, which leads to complete ignorance of temporal effects. In this contribution we present statistical and discretization techniques of partitioning the data recording time into intervals where the considered association rules remain homogeneous with respect to their support and confidence. In contrast with previous work where the considered time intervals are fixed they are determined in a data driven manner, which introduces a problem of optimal time granularity [2]. Furthermore, we demonstrate applicability of the risk-adjusted quality assessment in medical domain, specifically as it relates to heart surgery. For example, in comparison with the Euroscore risk system [3] the outcome prediction models for duration of intensive care or mortality can be significantly enhanced. Interesting pattern changes can be identified and assigned to systematical and organizational modifications of the considered system.