Detecting Temporal Change in Event Sequences: An Application to Demographic Data

In this paper, we discuss an approach for discovering temporal changes in event sequences, and present rst results from a study on demographic data. The data encode characteristic events in a per-son's life course, such as their birth date, the begin and end dates of their partnerships and marriages, and the birth dates of their children. The goal is to detect signiicant changes in the chronology of these events over people from diierent birth cohorts. To solve this problem, we encoded the temporal information in a rst-order logic representation, and employed Warmr, an ILP system that discovers association rules in a multi-relational data set, to detect frequent patterns that show signiicant variance over diierent birth cohorts. As a case study in multi-relational association rule mining, this work illustrates the exibility resulting from the use of rst-order background knowledge, but also uncovers a number of important issues that hitherto received little attention.