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dc.contributor.authorDlugosz, Stephanen_US
dc.date.accessioned2011-03-14en_US
dc.date.accessioned2011-03-14T15:33:18Z-
dc.date.available2011-03-14T15:33:18Z-
dc.date.issued2011en_US
dc.identifier.piurn:nbn:de:bsz:180-madoc-31434-
dc.identifier.urihttp://hdl.handle.net/10419/44458-
dc.description.abstractA new algorithm for clustering life course trajectories is presented and tested with large register data. Life courses are represented as sequences on a monthly timescale for the working-life with an age span from 16-65. A meaningful clustering result for this kind of data provides interesting subgroups with similar life course trajectories. The high sampling rate allows precise discrimination of the different subgroups, but it produces a lot of highly correlated data for phases with low variability. The main challenge is to select the variables (points in time) that carry most of the relevant information. The new algorithm deals with this problem by simultaneously clustering and identifying critical junctures for each of the relevant subgroups. The developed divisive algorithm is able to handle large amounts of data with multiple dimensions within reasonable time. This is demonstrated on data from the Federal German pension insurance.en_US
dc.language.isoengen_US
dc.publisherZentrum für Europäische Wirtschaftsforschung (ZEW) Mannheimen_US
dc.relation.ispartofseriesZEW Discussion Papers 11-015en_US
dc.subject.jelC33en_US
dc.subject.jelC38en_US
dc.subject.jelJ00en_US
dc.subject.ddc330en_US
dc.subject.keywordClusteringen_US
dc.subject.keywordmeasures of associationen_US
dc.subject.keyworddiscrete dataen_US
dc.subject.keywordtime seriesen_US
dc.titleClustering life trajectories: A new divisive hierarchical clustering algorithm for discrete-valued discrete time seriesen_US
dc.typeWorking Paperen_US
dc.identifier.ppn654047626en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen_US
dc.identifier.repecRePEc:zbw:zewdip:11015-
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ZEW Discussion Papers

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