Abstract
A procedure for segmentation by a constrained hierarchical clustering algorithm is proposed, using a criterion (or response) variable X and k structural factors or predictors, which yields classes different mainly as to the (conditional) distributions of X, computed within each segment. Since the procedure works on combinations of factor levels (and only indirectly on individuals), the methodology can be employed even for very large populations, with no increase of computational complexity.
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© 1998 Springer-Verlag Berlin · Heidelberg
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Gastaldi, T., Vicari, D. (1998). A constrained clusterwise procedure for segmentation. In: Rizzi, A., Vichi, M., Bock, HH. (eds) Advances in Data Science and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-72253-0_20
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DOI: https://doi.org/10.1007/978-3-642-72253-0_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-64641-9
Online ISBN: 978-3-642-72253-0
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