Abstract
[Mucha and Haimerl (2005)] proposed an algorithm to determine the stability of clusters found in hierarchical cluster analysis (HCA) and to calculate the rate of recovery by which an element can be reassigned to the same cluster in successive classifications of bootstrap samples. As proof of the concept this algorithm was applied to quantitative linguistics data. These investigations used only HCA algorithms. This paper will take a broader look at the stability of clustering results, and it will take different cluster algorithms into account; e.g. we compare the stability values of partitions from HCA with results from partitioning algorithms. To ease the comparison, the same data set - from dialect research of Northern Italy, as in [Mucha and Haimerl (2005)] - will be used here.
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Haimerl, E., Mucha, HJ. (2007). Comparing the Stability of Different Clustering Results of Dialect Data. In: Decker, R., Lenz, H.J. (eds) Advances in Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70981-7_71
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DOI: https://doi.org/10.1007/978-3-540-70981-7_71
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