Abstract
In this paper, new methodologies for clustering and dimensionality reduction of large data sets are illustrated. Two major types of data reduction methodologies are considered. The first are based on the simultaneous clustering of each mode of the observed multi-way data. The second are based on a clustering of the object mode to obtain mean profiles (centroids) and a factorial reduction of the other modes. These methodologies are described by a real application.
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© 2005 Springer-Verlag Berlin · Heidelberg
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Vichi, M. (2005). Clustering Including Dimensionality Reduction. 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_18
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DOI: https://doi.org/10.1007/3-540-28397-8_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26007-3
Online ISBN: 978-3-540-28397-3
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