Summary
Cluster analysis is specially concerned with algorithms for computing non-overlapping classifications, for example partitions or hierarchies, on given object sets. For several economic problems the determination of non-overlapping classifications representing the structure of data is too specific and narrow. In opposition to that given natural overlappings should not be suppressed because the construction of overlapping clusters gives a better insight into the structure of data. In this paper several principles of constructing overlapping clusters e.g. maximal cliques, fuzzy clustering, quasi-hierarchies and pyramidal classification are presented. The advantages and disadvantages of these clumping techniques are discussed in an overlapping clustering of selected software packages.
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© 1996 Springer-Verlag Berlin · Heidelberg
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Lasch, R. (1996). Overlapping Clustering of Statistical Software Packages for PC. 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_31
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DOI: https://doi.org/10.1007/978-3-642-79999-0_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-60354-2
Online ISBN: 978-3-642-79999-0
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