Other Paper

Detecting temporal change in event sequences: an application to demographic data

Blockeel, H., Fürnkranz, J., Prskawetz, A., Billari, F. C.
Also published in: L.De Raedt and A. Siebes (eds.) Principles of Data Mining and Knowledge Discovery, Lecture Notes in Artificial Intelligence 2168, Springer, Berlin, 29-41. (2001)


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 [AUTHORS]
The Max Planck Institute for Demographic Research (MPIDR) in Rostock is one of the leading demographic research centers in the world. It's part of the Max Planck Society, the internationally renowned German research society.