Abstract
A contingency table summarizes the conditional frequencies of two attributes and shows how these two attributes are dependent on each other. Thus, this table is a fundamental tool for pattern discovery with conditional probabilities, such as rule discovery. In this paper, a contingency table is interpreted from the viewpoint of granular computing. The first important observation is that a contingency table compares two attributes with respect to the number of equivalence classes. The second important observation is that matrix algebra is a key point of analysis of this table. Especially, the degree of independence, rank plays a very important role in extracting a probabilistic model from a given contingency table.
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Tsumoto, S. Statistical Independence as Linear Dependence in a Contingency Table. In: Young Lin, T., Ohsuga, S., Liau, CJ., Hu, X. (eds) Foundations and Novel Approaches in Data Mining. Studies in Computational Intelligence, vol 9. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539827_4
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DOI: https://doi.org/10.1007/11539827_4
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Publisher Name: Springer, Berlin, Heidelberg
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