Statistical Independence and Contingency Matrix
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
Fundamenta Informaticae - Cognitive Informatics, Cognitive Computing, and Their Denotational Mathematical Foundations (I)
Contingency Matrix Theory II: Degree of Dependence as Granularity
Fundamenta Informaticae - Cognitive Informatics, Cognitive Computing, and Their Denotational Mathematical Foundations (II)
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This paper gives a empirical analysis of determinant, which empirically validates the trade-off between sample size and size of matrix. In the former studies, relations between degree of granularity and dependence of contingency tables are given from the viewpoint of determinantal divisors and sample size. The nature of determinantal divisors shows that the increase of the degree of granularity may lead to that of dependence. However, a constraint on the sample size of a contingency table is very strong, which leads to the evaluation formula where the increase of degree of granularity gives the decrease of dependency. This paper gives a further study of the nature of sample size effect on the degree of dependency in a contingency matrix. The results show that sample size will restrict the nature of matrix in a combinatorial way, which suggests that the dependency is closely related with integer programming.