Abstract
A comprehensive simulation study recently has shown that, in order to identify best two-mode classifications, the user may apply different algorithms and select the result yielding the lowest squared centroid distance measurement (SCD). Knowing the outperformer among several goodness-of-fit measures creates the premises to develop an exchange algorithm for two-mode classifications. This paper presents the algorithm and discusses significance in gain of precision based on a large Monte Carlo Simulation study.
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© 2005 Springer-Verlag Berlin · Heidelberg
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Schwaiger, M., Rix, R. (2005). An Exchange Algorithm for Two-Mode Cluster Analysis. In: Baier, D., Wernecke, KD. (eds) Innovations in Classification, Data Science, and Information Systems. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-26981-9_8
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DOI: https://doi.org/10.1007/3-540-26981-9_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23221-6
Online ISBN: 978-3-540-26981-6
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