Mining event histories: a social science perspective

Gilbert Ritschard, Alexis Gabadinho, Nicolas S. Muller and Matthias Studer

International Journal of Data Mining, Modelling and Management, 2008, vol. 1, issue 1, 68-90

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. (search for similar items in EconPapers)
Date: 2008
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Citations: View citations in EconPapers (3)

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