Abstract
In this paper methods to cluster analyze two-mode data are discussed which assume that both objects and attributes contribute to the uncovering of meaningful patterns of clusters. Two-mode methods are reviewed and criteria are proposed which aim at a comparison and evaluation of the reviewed methods. The selected criteria show that most two-mode approaches suffer from drawbacks concerning interpretation of the data, convergence of algorithms, uniqueness of solutions or applicability to larger data sets. They imply some suggestions for future directions in the development of two- and three-mode cluster analysis.
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Krolak-Schwerdt, S. (2003). Two-Mode Clustering Methods: Compare and Contrast. In: Schader, M., Gaul, W., Vichi, M. (eds) Between Data Science and Applied Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18991-3_31
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DOI: https://doi.org/10.1007/978-3-642-18991-3_31
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