Abstract
One of the most common tasks in (classical as well as symbolic) data analysis is the detection and construction of ‘homogeneous’ groups C1, C2,… of objects in a population Ω or E such that objects from the same group show a high similarity whereas objects from different groups are typically more dissimilar. Such groups are usually called ‘clusters’ and must be constructed on the basis of the (classical or symbolic) data which were recorded for the objects.
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© 2000 Springer-Verlag Berlin Heidelberg
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Chavent, M., Bock, HH. (2000). Clustering Methods for Symbolic Objects. In: Bock, HH., Diday, E. (eds) Analysis of Symbolic Data. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-57155-8_11
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DOI: https://doi.org/10.1007/978-3-642-57155-8_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66619-6
Online ISBN: 978-3-642-57155-8
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