Dialectometry produces aggregate DISTANCE MATRICES in which a distance is specified for each pair of sites. By projecting groups obtained by clustering onto geography one compares results with traditional dialectology, which produced maps partitioned into implicitly non-overlapping DIALECT AREAS. The importance of dialect areas has been challenged by proponents Of CONTINUA, but they too need to compare their findings to older literature, expressed in terms of areas.
Simple clustering is unstable, meaning that small differences in the input matrix can lead to large differences in results (Jain et al. 1999). This is illustrated with a 500-site data set from Bulgaria, where input matrices which correlate very highly (r = 0.97) still yield very different clusterings. Kleiweg et al. (2004) introduce COMPOSITE CLUSTERING, in which random noise is added to matrices during repeated clustering. The resulting borders are then projected onto the map.
The present contribution compares Kleiweg et al.'s procedure to resampled bootstrapping, and also shows how the same procedure used to project borders from composite clustering may be used to project borders from bootstrapping.
|Title of host publication||Data Analysis, Machine Learning and Applications. Proceedings of the 31st Annual Conference ofthe Gesellschaft für Klassifikation e.V., Albert-Ludwigs Universität Freiburg, March 7-9, 2007|
|Editors||C Preisach, H Burkhardt, L SchmidtThieme, R Decker|
|Place of Publication||BERLIN|
|Number of pages||8|
|Publication status||Published - 2008|
|Event||31st Annual Conference of the German-Classification-Society - , Germany|
Duration: 7-Mar-2007 → 9-Mar-2007
|Name||STUDIES IN CLASSIFICATION, DATA ANALYSIS, AND KNOWLEDGE ORGANIZATION|
|Other||31st Annual Conference of the German-Classification-Society|
|Period||07/03/2007 → 09/03/2007|