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Mining event histories: a social science perspective

Author

Listed:
  • Gilbert Ritschard
  • Alexis Gabadinho
  • Nicolas S. Muller
  • Matthias Studer

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.

Suggested Citation

  • Gilbert Ritschard & Alexis Gabadinho & Nicolas S. Muller & Matthias Studer, 2008. "Mining event histories: a social science perspective," International Journal of Data Mining, Modelling and Management, Inderscience Enterprises Ltd, vol. 1(1), pages 68-90.
    • Handle: RePEc:ids:ijdmmm:v:1:y:2008:i:1:p:68-90
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    Citations

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    Cited by:

    1. Hilde Bras & Reto Schumacher, 2019. "Changing gender relations, declining fertility? An analysis of childbearing trajectories in 19th-century Netherlands," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 41(30), pages 873-912.
    2. Mike Smet & Barbara Janssens, 2015. "Educational Pathways of students who enrolled in a subject-specific teacher training in Flanders: An Optimal Matching Approach," EcoMod2015 8577, EcoMod.
    3. Gabadinho, Alexis & Ritschard, Gilbert & Müller, Nicolas S & Studer, Matthias, 2011. "Analyzing and Visualizing State Sequences in R with TraMineR," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i04).

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