Title: Mining event histories: a social science perspective

Authors: Gilbert Ritschard, Alexis Gabadinho, Nicolas S. Muller, Matthias Studer

Addresses: Department of Econometrics and Laboratory of Demography, Faculty of Economics and Social Sciences, University of Geneva, 40, bd du Pont-d'Arve, CH-1211 Geneva, Switzerland. ' Department of Econometrics and Laboratory of Demography, Faculty of Economics and Social Sciences, University of Geneva, 40, bd du Pont-d'Arve, CH-1211 Geneva, Switzerland. ' Department of Econometrics and Laboratory of Demography, Faculty of Economics and Social Sciences, University of Geneva, 40, bd du Pont-d'Arve, CH-1211 Geneva, Switzerland. ' Department of Econometrics and Laboratory of Demography, Faculty of Economics and Social Sciences, University of Geneva, 40, bd du Pont-d'Arve, CH-1211 Geneva, Switzerland

Abstract: We explore how recent data mining-based tools developed in domains such as biomedicine or text mining for extracting interesting knowledge from sequence data could be applied to personal life course data. We focus on two types of approaches: |survival| trees that attempt to partition the data into homogeneous groups regarding their survival characteristics, i.e., the duration until a given event occurs and the mining of typical discriminating episodes. We show how these approaches may fruitfully complement the outcome of more classical event history analyses and single out some specific issues raised by their application to socio-demographic data.

Keywords: event histories; state sequences; event sequences; mining frequent episodes; discriminating subsequences; survival trees; social sciences; life course; longitudinal data; data mining; data modelling; data management; socio-demographic data; personal life course data.

DOI: 10.1504/IJDMMM.2008.022538

International Journal of Data Mining, Modelling and Management, 2008 Vol.1 No.1, pp.68 - 90

Published online: 14 Jan 2009 *

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