Abstract
A variety of algorithms do exist to perform two-mode cluster analysis, normally leading to different results based on the same data matrix. In this article we evaluate hierarchical two-mode algorithms by means of a large simulation study using a dominant performance measure.
To cover the wide spectrum of possible applications, synthetic classifications are created and processed with Monte-Carlo simulations. The appropriateness of the algorithms applied is then evaluated, showing that all algorithms are pretty well able to recover the original classification, if no perturbations are applied on the data base. Otherwise, considerable performance differences can be shown.
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Schwaiger, M., Rix, R. (2005). On the Performance of Algorithms for Two-Mode Hierarchical Cluster Analysis — Results from a Monte Carlo Simulation Study. In: Baier, D., Decker, R., Schmidt-Thieme, L. (eds) Data Analysis and Decision Support. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-28397-8_17
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DOI: https://doi.org/10.1007/3-540-28397-8_17
Publisher Name: Springer, Berlin, Heidelberg
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