Abstract
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Forms of representing and comparing partitions are reviewed.
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Mathematical analysis of some of the agglomerative clustering axioms is presented.
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Approximation clustering methods for aggregating square data tables are suggested along with associated mathematical theories:
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Uniform partitioning as based on a “soft” similarity threshold;
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Structured partitioning (along with the structure of between-class associations);
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Aggregation of mobility and other aggregable interaction data as based on chi-squared criterion and underlying substantive modeling.
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© 1996 Kluwer Academic Publishers
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Mirkin, B. (1996). Partition: Square Data Table. In: Mathematical Classification and Clustering. Nonconvex Optimization and Its Applications, vol 11. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0457-9_5
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DOI: https://doi.org/10.1007/978-1-4613-0457-9_5
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-8057-3
Online ISBN: 978-1-4613-0457-9
eBook Packages: Springer Book Archive