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OFAI-TR-2001-09 ( 88kB g-zipped PostScript file,  238kB PDF file)

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

Hendrik Blockeel, Johannes Fürnkranz, Alexia Prskawetz, Francesco C. Billari

In this paper, we discuss an approach for discovering temporal changes in event sequences, and present first results from a study on demographic data. The data encode characteristic events in a person'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 significant changes in the chronology of these events over people from different birth cohorts. To solve this problem, we encoded the temporal information in a first-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 significant variance over different birth cohorts. As a case study in multi-relational association rule mining, this work illustrates the flexibility resulting from the use of first-order background knowledge, but also uncovers a number of important issues that hitherto received little attention.

Keywords: Data Mining, Association Rules, Temporal Patterns, Inductive Logic Programming, Demography, Life Course Analysis

Citation: Blockeel H., Fürnkranz J., Prskawetz A., Billari F.: Detecting Temporal Change in Event Sequences: An Application to Demographic Data. In L. De Raedt and A. Siebes (Eds.), Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD-01), Freiburg, Germany. Springer-Verlag 2001.