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- W945546923 abstract "An ordinal approach to the a posteriori evaluation of the explanatory power of variables in classifications is proposed. The contribution of each variable is assessed in a way fully compatible with the distance or dissimilarity function used in the clustering process. Then, a simple ranking-based measure is applied to express the relative agreement or disagreement of variables with a given partition. This measure treats all variables equally, no matter how influential they were when the classification was actually created. The sum of measures for all variables reflects their overall agreement and can be used to select an optimal partition from a hierarchical classification." @default.
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- W945546923 date "1998-01-01" @default.
- W945546923 modified "2023-10-16" @default.
- W945546923 title "Explanatory Variables in Classifications and the Detection of the Optimum Number of Clusters" @default.
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- W945546923 doi "https://doi.org/10.1007/978-4-431-65950-1_11" @default.
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