Abstract
The strength of association between the row and column variables in a cross table varies with the level of aggregation of each variable. In many settings like the simultaneous discretization of two variables, it is useful to determine the aggregation level that maximizes the association. This paper deals with the behavior of association measures with respect to the aggregation of rows and columns and proposes an heuristic algorithm to (quasi-)maximize the association through aggregation.
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Ritschard, G., Nicoloyannis, N. (2000). Aggregation and Association in Cross Tables. In: Zighed, D.A., Komorowski, J., Żytkow, J. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 2000. Lecture Notes in Computer Science(), vol 1910. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45372-5_71
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DOI: https://doi.org/10.1007/3-540-45372-5_71
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