Summary
In recent years several models and corresponding algorithms for clustering two- or higher-mode data have been developed, including the additive-clustering approach (e.g., DeSarbo, 1982), the tree-fitting approach (e.g., De Soete & Carroll, 1989), and the error-variance approach (e.g., Eckes & Orlik, 1993). The present paper relates various types of data frequently collected in the behavioral and social sciences to prominent models of multimode clustering and demonstrates the versatility of three-mode clustering using a real data set drawn from social-psychological research.
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© 1996 Springer-Verlag Berlin · Heidelberg
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Eckes, T. (1996). Recent Developments in Multimode Clustering. In: Gaul, W., Pfeifer, D. (eds) From Data to Knowledge. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-79999-0_14
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DOI: https://doi.org/10.1007/978-3-642-79999-0_14
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